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<a href="#nested-classes">Classes</a> &#124;
<a href="#define-members">Macros</a>  </div>
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<div class="title">detectNet<div class="ingroups"><a class="el" href="group__deepVision.html">DNN Vision Library (jetson-inference)</a></div></div>  </div>
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<p>Object detection DNN (SSD, DetectNet)  
<a href="#details">More...</a></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:classdetectNet"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#classdetectNet">detectNet</a></td></tr>
<tr class="memdesc:classdetectNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Object recognition and localization networks with TensorRT support.  <a href="group__detectNet.html#classdetectNet">More...</a><br /></td></tr>
<tr class="separator:classdetectNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="define-members"></a>
Macros</h2></td></tr>
<tr class="memitem:gac824e329015dc8aed6e1112bfe21cb97"><td class="memItemLeft" align="right" valign="top">#define&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#gac824e329015dc8aed6e1112bfe21cb97">DETECTNET_DEFAULT_INPUT</a>&#160;&#160;&#160;&quot;data&quot;</td></tr>
<tr class="memdesc:gac824e329015dc8aed6e1112bfe21cb97"><td class="mdescLeft">&#160;</td><td class="mdescRight">Name of default input blob for DetectNet caffe model.  <a href="group__detectNet.html#gac824e329015dc8aed6e1112bfe21cb97">More...</a><br /></td></tr>
<tr class="separator:gac824e329015dc8aed6e1112bfe21cb97"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga1e79603783719e4a79f2c68f1ef47621"><td class="memItemLeft" align="right" valign="top">#define&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#ga1e79603783719e4a79f2c68f1ef47621">DETECTNET_DEFAULT_COVERAGE</a>&#160;&#160;&#160;&quot;coverage&quot;</td></tr>
<tr class="memdesc:ga1e79603783719e4a79f2c68f1ef47621"><td class="mdescLeft">&#160;</td><td class="mdescRight">Name of default output blob of the coverage map for DetectNet caffe model.  <a href="group__detectNet.html#ga1e79603783719e4a79f2c68f1ef47621">More...</a><br /></td></tr>
<tr class="separator:ga1e79603783719e4a79f2c68f1ef47621"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gac8c52a38ffa041865ed71fd2ea806620"><td class="memItemLeft" align="right" valign="top">#define&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#gac8c52a38ffa041865ed71fd2ea806620">DETECTNET_DEFAULT_BBOX</a>&#160;&#160;&#160;&quot;bboxes&quot;</td></tr>
<tr class="memdesc:gac8c52a38ffa041865ed71fd2ea806620"><td class="mdescLeft">&#160;</td><td class="mdescRight">Name of default output blob of the grid of bounding boxes for DetectNet caffe model.  <a href="group__detectNet.html#gac8c52a38ffa041865ed71fd2ea806620">More...</a><br /></td></tr>
<tr class="separator:gac8c52a38ffa041865ed71fd2ea806620"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga56785ae0440a9e281a6dc44dc01039a5"><td class="memItemLeft" align="right" valign="top">#define&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#ga56785ae0440a9e281a6dc44dc01039a5">DETECTNET_DEFAULT_CONFIDENCE_THRESHOLD</a>&#160;&#160;&#160;0.5f</td></tr>
<tr class="memdesc:ga56785ae0440a9e281a6dc44dc01039a5"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default value of the minimum detection threshold.  <a href="group__detectNet.html#ga56785ae0440a9e281a6dc44dc01039a5">More...</a><br /></td></tr>
<tr class="separator:ga56785ae0440a9e281a6dc44dc01039a5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gaa2efb4303553391c619798a21b0f0d4e"><td class="memItemLeft" align="right" valign="top">#define&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#gaa2efb4303553391c619798a21b0f0d4e">DETECTNET_DEFAULT_CLUSTERING_THRESHOLD</a>&#160;&#160;&#160;0.75f</td></tr>
<tr class="memdesc:gaa2efb4303553391c619798a21b0f0d4e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default value of the clustering area-of-overlap threshold.  <a href="group__detectNet.html#gaa2efb4303553391c619798a21b0f0d4e">More...</a><br /></td></tr>
<tr class="separator:gaa2efb4303553391c619798a21b0f0d4e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga5de620e838c2ac9aec16c6de2977513f"><td class="memItemLeft" align="right" valign="top">#define&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#ga5de620e838c2ac9aec16c6de2977513f">DETECTNET_DEFAULT_THRESHOLD</a>&#160;&#160;&#160;<a class="el" href="group__detectNet.html#ga56785ae0440a9e281a6dc44dc01039a5">DETECTNET_DEFAULT_CONFIDENCE_THRESHOLD</a></td></tr>
<tr class="memdesc:ga5de620e838c2ac9aec16c6de2977513f"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default value of the minimum detection threshold.  <a href="group__detectNet.html#ga5de620e838c2ac9aec16c6de2977513f">More...</a><br /></td></tr>
<tr class="separator:ga5de620e838c2ac9aec16c6de2977513f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga98aed3513105a81bd86361eef5423383"><td class="memItemLeft" align="right" valign="top">#define&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#ga98aed3513105a81bd86361eef5423383">DETECTNET_DEFAULT_ALPHA</a>&#160;&#160;&#160;120</td></tr>
<tr class="memdesc:ga98aed3513105a81bd86361eef5423383"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default alpha blending value used during overlay.  <a href="group__detectNet.html#ga98aed3513105a81bd86361eef5423383">More...</a><br /></td></tr>
<tr class="separator:ga98aed3513105a81bd86361eef5423383"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gaa3c79af5c5926692f4ee973b3a7e1a86"><td class="memItemLeft" align="right" valign="top">#define&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#gaa3c79af5c5926692f4ee973b3a7e1a86">DETECTNET_MODEL_TYPE</a>&#160;&#160;&#160;&quot;detection&quot;</td></tr>
<tr class="memdesc:gaa3c79af5c5926692f4ee973b3a7e1a86"><td class="mdescLeft">&#160;</td><td class="mdescRight">The model type for <a class="el" href="group__detectNet.html#classdetectNet" title="Object recognition and localization networks with TensorRT support.">detectNet</a> in data/networks/models.json.  <a href="group__detectNet.html#gaa3c79af5c5926692f4ee973b3a7e1a86">More...</a><br /></td></tr>
<tr class="separator:gaa3c79af5c5926692f4ee973b3a7e1a86"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gad5f43885a04689f10c6f9d297ab88a8d"><td class="memItemLeft" align="right" valign="top">#define&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#gad5f43885a04689f10c6f9d297ab88a8d">DETECTNET_USAGE_STRING</a></td></tr>
<tr class="memdesc:gad5f43885a04689f10c6f9d297ab88a8d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Standard command-line options able to be passed to <a class="el" href="group__detectNet.html#a468b5e636096159500f3db327c31e8dd" title="Load a pre-trained model.">detectNet::Create()</a>  <a href="group__detectNet.html#gad5f43885a04689f10c6f9d297ab88a8d">More...</a><br /></td></tr>
<tr class="separator:gad5f43885a04689f10c6f9d297ab88a8d"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<p>Object detection DNN (SSD, DetectNet) </p>
<hr/><h2 class="groupheader">Class Documentation</h2>
<a name="classdetectNet" id="classdetectNet"></a>
<h2 class="memtitle"><span class="permalink"><a href="#classdetectNet">&#9670;&nbsp;</a></span>detectNet</h2>

<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">class detectNet</td>
        </tr>
      </table>
</div><div class="memdoc">
<div class="textblock"><p>Object recognition and localization networks with TensorRT support. </p>
</div><div class="dynheader">
Inheritance diagram for detectNet:</div>
<div class="dyncontent">
 <div class="center">
  <img src="group__detectNet.png" usemap="#detectNet_map" alt=""/>
  <map id="detectNet_map" name="detectNet_map">
<area href="group__tensorNet.html#classtensorNet" title="Abstract class for loading a tensor network with TensorRT." alt="tensorNet" shape="rect" coords="0,0,66,24"/>
  </map>
</div></div>
<table class="memberdecls">
<tr><td colspan="2"><h3>Public Types</h3></td></tr>
<tr class="memitem:a29e74cde23a8dd541dbd848e457663d6"><td class="memItemLeft" align="right" valign="top">enum &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6">OverlayFlags</a> { <br />
&#160;&#160;<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6a966eaaaa69ed08d36dcb862e3fc644bf">OVERLAY_NONE</a> = 0, 
<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6aff23b9098d5bf0f91c31954001c2ff68">OVERLAY_BOX</a> = (1 &lt;&lt; 0), 
<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6a7eb5454e7a6dd53d90c2ddd33b00d049">OVERLAY_LABEL</a> = (1 &lt;&lt; 1), 
<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6af7ce69cd21d84fb3b963713da66756eb">OVERLAY_CONFIDENCE</a> = (1 &lt;&lt; 2), 
<br />
&#160;&#160;<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6a83823d45cf424993d605fd9705378ace">OVERLAY_TRACKING</a> = (1 &lt;&lt; 3), 
<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6af5f8642765b53b65fea273be879a5ab4">OVERLAY_LINES</a> = (1 &lt;&lt; 4), 
<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a> = OVERLAY_BOX|OVERLAY_LABEL|OVERLAY_CONFIDENCE
<br />
 }</td></tr>
<tr class="memdesc:a29e74cde23a8dd541dbd848e457663d6"><td class="mdescLeft">&#160;</td><td class="mdescRight">Overlay flags (can be OR'd together).  <a href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6">More...</a><br /></td></tr>
<tr class="separator:a29e74cde23a8dd541dbd848e457663d6"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr><td colspan="2"><h3>Public Member Functions</h3></td></tr>
<tr class="memitem:a82c0e177e0da142fa22aa7580077307d"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a82c0e177e0da142fa22aa7580077307d">~detectNet</a> ()</td></tr>
<tr class="memdesc:a82c0e177e0da142fa22aa7580077307d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destory.  <a href="group__detectNet.html#a82c0e177e0da142fa22aa7580077307d">More...</a><br /></td></tr>
<tr class="separator:a82c0e177e0da142fa22aa7580077307d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a158f5cf9d366668b8229375c1a342a04"><td class="memTemplParams" colspan="2">template&lt;typename T &gt; </td></tr>
<tr class="memitem:a158f5cf9d366668b8229375c1a342a04"><td class="memTemplItemLeft" align="right" valign="top">int&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a158f5cf9d366668b8229375c1a342a04">Detect</a> (T *image, uint32_t width, uint32_t height, <a class="el" href="structdetectNet_1_1Detection.html">Detection</a> **detections, uint32_t overlay=<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a>)</td></tr>
<tr class="memdesc:a158f5cf9d366668b8229375c1a342a04"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect object locations from an image, returning an array containing the detection results.  <a href="group__detectNet.html#a158f5cf9d366668b8229375c1a342a04">More...</a><br /></td></tr>
<tr class="separator:a158f5cf9d366668b8229375c1a342a04"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3cd0725bfab4ec8a54192eae4c7bac45"><td class="memTemplParams" colspan="2">template&lt;typename T &gt; </td></tr>
<tr class="memitem:a3cd0725bfab4ec8a54192eae4c7bac45"><td class="memTemplItemLeft" align="right" valign="top">int&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a3cd0725bfab4ec8a54192eae4c7bac45">Detect</a> (T *image, uint32_t width, uint32_t height, <a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *detections, uint32_t overlay=<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a>)</td></tr>
<tr class="memdesc:a3cd0725bfab4ec8a54192eae4c7bac45"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect object locations in an image, into an array of the results allocated by the user.  <a href="group__detectNet.html#a3cd0725bfab4ec8a54192eae4c7bac45">More...</a><br /></td></tr>
<tr class="separator:a3cd0725bfab4ec8a54192eae4c7bac45"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3974d2ac560544539ba98b48fa736f4e"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a3974d2ac560544539ba98b48fa736f4e">Detect</a> (void *input, uint32_t width, uint32_t height, <a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a> format, <a class="el" href="structdetectNet_1_1Detection.html">Detection</a> **detections, uint32_t overlay=<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a>)</td></tr>
<tr class="memdesc:a3974d2ac560544539ba98b48fa736f4e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect object locations from an image, returning an array containing the detection results.  <a href="group__detectNet.html#a3974d2ac560544539ba98b48fa736f4e">More...</a><br /></td></tr>
<tr class="separator:a3974d2ac560544539ba98b48fa736f4e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a49c6fcf052819b9980fd9d72b09d26c3"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a49c6fcf052819b9980fd9d72b09d26c3">Detect</a> (void *input, uint32_t width, uint32_t height, <a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a> format, <a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *detections, uint32_t overlay=<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a>)</td></tr>
<tr class="memdesc:a49c6fcf052819b9980fd9d72b09d26c3"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect object locations from an image, into an array of the results allocated by the user.  <a href="group__detectNet.html#a49c6fcf052819b9980fd9d72b09d26c3">More...</a><br /></td></tr>
<tr class="separator:a49c6fcf052819b9980fd9d72b09d26c3"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae29e82df844140f88d4d369f56886142"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#ae29e82df844140f88d4d369f56886142">Detect</a> (float *input, uint32_t width, uint32_t height, <a class="el" href="structdetectNet_1_1Detection.html">Detection</a> **detections, uint32_t overlay=<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a>)</td></tr>
<tr class="memdesc:ae29e82df844140f88d4d369f56886142"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect object locations from an RGBA image, returning an array containing the detection results.  <a href="group__detectNet.html#ae29e82df844140f88d4d369f56886142">More...</a><br /></td></tr>
<tr class="separator:ae29e82df844140f88d4d369f56886142"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a20ceb077cd42eaa2ebc0bdb31e68e10c"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a20ceb077cd42eaa2ebc0bdb31e68e10c">Detect</a> (float *input, uint32_t width, uint32_t height, <a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *detections, uint32_t overlay=<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a>)</td></tr>
<tr class="memdesc:a20ceb077cd42eaa2ebc0bdb31e68e10c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect object locations in an RGBA image, into an array of the results allocated by the user.  <a href="group__detectNet.html#a20ceb077cd42eaa2ebc0bdb31e68e10c">More...</a><br /></td></tr>
<tr class="separator:a20ceb077cd42eaa2ebc0bdb31e68e10c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a94ff26d023e725a5c2da5be4f8d237d5"><td class="memTemplParams" colspan="2">template&lt;typename T &gt; </td></tr>
<tr class="memitem:a94ff26d023e725a5c2da5be4f8d237d5"><td class="memTemplItemLeft" align="right" valign="top">bool&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a94ff26d023e725a5c2da5be4f8d237d5">Overlay</a> (T *input, T *output, uint32_t width, uint32_t height, <a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *detections, uint32_t numDetections, uint32_t flags=<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a>)</td></tr>
<tr class="memdesc:a94ff26d023e725a5c2da5be4f8d237d5"><td class="mdescLeft">&#160;</td><td class="mdescRight">Draw the detected bounding boxes overlayed on an RGBA image.  <a href="group__detectNet.html#a94ff26d023e725a5c2da5be4f8d237d5">More...</a><br /></td></tr>
<tr class="separator:a94ff26d023e725a5c2da5be4f8d237d5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af6bfc51166410901174b48f99f59f85b"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#af6bfc51166410901174b48f99f59f85b">Overlay</a> (void *input, void *output, uint32_t width, uint32_t height, <a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a> format, <a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *detections, uint32_t numDetections, uint32_t flags=<a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a>)</td></tr>
<tr class="memdesc:af6bfc51166410901174b48f99f59f85b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Draw the detected bounding boxes overlayed on an RGBA image.  <a href="group__detectNet.html#af6bfc51166410901174b48f99f59f85b">More...</a><br /></td></tr>
<tr class="separator:af6bfc51166410901174b48f99f59f85b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad50b3774ee7350d0c256d94a785ba9df"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#ad50b3774ee7350d0c256d94a785ba9df">GetThreshold</a> () const</td></tr>
<tr class="memdesc:ad50b3774ee7350d0c256d94a785ba9df"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the minimum threshold for detection.  <a href="group__detectNet.html#ad50b3774ee7350d0c256d94a785ba9df">More...</a><br /></td></tr>
<tr class="separator:ad50b3774ee7350d0c256d94a785ba9df"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1cb45695bf95d5fb5db48976d1de8ae2"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a1cb45695bf95d5fb5db48976d1de8ae2">SetThreshold</a> (float threshold)</td></tr>
<tr class="memdesc:a1cb45695bf95d5fb5db48976d1de8ae2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the minimum threshold for detection.  <a href="group__detectNet.html#a1cb45695bf95d5fb5db48976d1de8ae2">More...</a><br /></td></tr>
<tr class="separator:a1cb45695bf95d5fb5db48976d1de8ae2"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9bb59636a3a0ba168752bb5e9f76421c"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a9bb59636a3a0ba168752bb5e9f76421c">GetConfidenceThreshold</a> () const</td></tr>
<tr class="memdesc:a9bb59636a3a0ba168752bb5e9f76421c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the minimum threshold for detection.  <a href="group__detectNet.html#a9bb59636a3a0ba168752bb5e9f76421c">More...</a><br /></td></tr>
<tr class="separator:a9bb59636a3a0ba168752bb5e9f76421c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7d9740240d5def31cdbb92c90fb3f26f"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a7d9740240d5def31cdbb92c90fb3f26f">SetConfidenceThreshold</a> (float threshold)</td></tr>
<tr class="memdesc:a7d9740240d5def31cdbb92c90fb3f26f"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the minimum threshold for detection.  <a href="group__detectNet.html#a7d9740240d5def31cdbb92c90fb3f26f">More...</a><br /></td></tr>
<tr class="separator:a7d9740240d5def31cdbb92c90fb3f26f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae63aba1ee4e12f8afab35f0c928f4ef6"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#ae63aba1ee4e12f8afab35f0c928f4ef6">GetClusteringThreshold</a> () const</td></tr>
<tr class="memdesc:ae63aba1ee4e12f8afab35f0c928f4ef6"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the overlapping area % threshold for clustering.  <a href="group__detectNet.html#ae63aba1ee4e12f8afab35f0c928f4ef6">More...</a><br /></td></tr>
<tr class="separator:ae63aba1ee4e12f8afab35f0c928f4ef6"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a43266dab4ba9e3115c44df93ee2f34d8"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a43266dab4ba9e3115c44df93ee2f34d8">SetClusteringThreshold</a> (float threshold)</td></tr>
<tr class="memdesc:a43266dab4ba9e3115c44df93ee2f34d8"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the overlapping area % threshold for clustering.  <a href="group__detectNet.html#a43266dab4ba9e3115c44df93ee2f34d8">More...</a><br /></td></tr>
<tr class="separator:a43266dab4ba9e3115c44df93ee2f34d8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a130307a2bac02b13834116eb72728530"><td class="memItemLeft" align="right" valign="top"><a class="el" href="group__objectTracker.html#classobjectTracker">objectTracker</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a130307a2bac02b13834116eb72728530">GetTracker</a> () const</td></tr>
<tr class="memdesc:a130307a2bac02b13834116eb72728530"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the object tracker being used.  <a href="group__detectNet.html#a130307a2bac02b13834116eb72728530">More...</a><br /></td></tr>
<tr class="separator:a130307a2bac02b13834116eb72728530"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a83cf2684627e8b6f3d14eee8f02def61"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a83cf2684627e8b6f3d14eee8f02def61">SetTracker</a> (<a class="el" href="group__objectTracker.html#classobjectTracker">objectTracker</a> *tracker)</td></tr>
<tr class="memdesc:a83cf2684627e8b6f3d14eee8f02def61"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the object tracker to be used.  <a href="group__detectNet.html#a83cf2684627e8b6f3d14eee8f02def61">More...</a><br /></td></tr>
<tr class="separator:a83cf2684627e8b6f3d14eee8f02def61"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a38689b7d4b83723038677d66c1961974"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a38689b7d4b83723038677d66c1961974">GetMaxDetections</a> () const</td></tr>
<tr class="memdesc:a38689b7d4b83723038677d66c1961974"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the maximum number of simultaneous detections the network supports.  <a href="group__detectNet.html#a38689b7d4b83723038677d66c1961974">More...</a><br /></td></tr>
<tr class="separator:a38689b7d4b83723038677d66c1961974"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acec72d01e020cf5bc50747abb0245f06"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#acec72d01e020cf5bc50747abb0245f06">GetNumClasses</a> () const</td></tr>
<tr class="memdesc:acec72d01e020cf5bc50747abb0245f06"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the number of object classes supported in the detector.  <a href="group__detectNet.html#acec72d01e020cf5bc50747abb0245f06">More...</a><br /></td></tr>
<tr class="separator:acec72d01e020cf5bc50747abb0245f06"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a80524a777ce4fc368922ac3cd8f97973"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a80524a777ce4fc368922ac3cd8f97973">GetClassLabel</a> (uint32_t index) const</td></tr>
<tr class="memdesc:a80524a777ce4fc368922ac3cd8f97973"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the description of a particular class.  <a href="group__detectNet.html#a80524a777ce4fc368922ac3cd8f97973">More...</a><br /></td></tr>
<tr class="separator:a80524a777ce4fc368922ac3cd8f97973"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6aeee309257b1a42df275fdfe948a585"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a6aeee309257b1a42df275fdfe948a585">GetClassDesc</a> (uint32_t index) const</td></tr>
<tr class="memdesc:a6aeee309257b1a42df275fdfe948a585"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the description of a particular class.  <a href="group__detectNet.html#a6aeee309257b1a42df275fdfe948a585">More...</a><br /></td></tr>
<tr class="separator:a6aeee309257b1a42df275fdfe948a585"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a68322545826d7b866a70d5f9758f4d48"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a68322545826d7b866a70d5f9758f4d48">GetClassSynset</a> (uint32_t index) const</td></tr>
<tr class="memdesc:a68322545826d7b866a70d5f9758f4d48"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the class synset category of a particular class.  <a href="group__detectNet.html#a68322545826d7b866a70d5f9758f4d48">More...</a><br /></td></tr>
<tr class="separator:a68322545826d7b866a70d5f9758f4d48"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8085235ab275b1bad3dcde14ec1223ed"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a8085235ab275b1bad3dcde14ec1223ed">GetClassPath</a> () const</td></tr>
<tr class="memdesc:a8085235ab275b1bad3dcde14ec1223ed"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the path to the file containing the class descriptions.  <a href="group__detectNet.html#a8085235ab275b1bad3dcde14ec1223ed">More...</a><br /></td></tr>
<tr class="separator:a8085235ab275b1bad3dcde14ec1223ed"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad099b492b82a1d7a55955ecf990fbea8"><td class="memItemLeft" align="right" valign="top">float4&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#ad099b492b82a1d7a55955ecf990fbea8">GetClassColor</a> (uint32_t classIndex) const</td></tr>
<tr class="memdesc:ad099b492b82a1d7a55955ecf990fbea8"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the RGBA visualization color a particular class.  <a href="group__detectNet.html#ad099b492b82a1d7a55955ecf990fbea8">More...</a><br /></td></tr>
<tr class="separator:ad099b492b82a1d7a55955ecf990fbea8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afac4f7b876a6ee391599bbce3f2982b1"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#afac4f7b876a6ee391599bbce3f2982b1">SetClassColor</a> (uint32_t classIndex, const float4 &amp;<a class="el" href="cudaPointCloud_8h.html#a549d5369c70bd71a70798aa5b1ea4270">color</a>)</td></tr>
<tr class="memdesc:afac4f7b876a6ee391599bbce3f2982b1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the visualization color of a particular class of object.  <a href="group__detectNet.html#afac4f7b876a6ee391599bbce3f2982b1">More...</a><br /></td></tr>
<tr class="separator:afac4f7b876a6ee391599bbce3f2982b1"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab9ba7c63d2fc417d0908324fd0dc2223"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#ab9ba7c63d2fc417d0908324fd0dc2223">SetClassColor</a> (uint32_t classIndex, float r, float g, float b, float a=255.0f)</td></tr>
<tr class="memdesc:ab9ba7c63d2fc417d0908324fd0dc2223"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the visualization color of a particular class of object.  <a href="group__detectNet.html#ab9ba7c63d2fc417d0908324fd0dc2223">More...</a><br /></td></tr>
<tr class="separator:ab9ba7c63d2fc417d0908324fd0dc2223"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6259a0c37ec3a3246bf23aad152c69e4"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a6259a0c37ec3a3246bf23aad152c69e4">GetLineWidth</a> () const</td></tr>
<tr class="memdesc:a6259a0c37ec3a3246bf23aad152c69e4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the line width used during overlay when OVERLAY_LINES is used.  <a href="group__detectNet.html#a6259a0c37ec3a3246bf23aad152c69e4">More...</a><br /></td></tr>
<tr class="separator:a6259a0c37ec3a3246bf23aad152c69e4"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0e8b7f336a5531ce9446b4c63e7143e4"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a0e8b7f336a5531ce9446b4c63e7143e4">SetLineWidth</a> (float width)</td></tr>
<tr class="memdesc:a0e8b7f336a5531ce9446b4c63e7143e4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the line width used during overlay when OVERLAY_LINES is used.  <a href="group__detectNet.html#a0e8b7f336a5531ce9446b4c63e7143e4">More...</a><br /></td></tr>
<tr class="separator:a0e8b7f336a5531ce9446b4c63e7143e4"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aefb9214feb29121368dfaa5cf4082565"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#aefb9214feb29121368dfaa5cf4082565">GetOverlayAlpha</a> () const</td></tr>
<tr class="memdesc:aefb9214feb29121368dfaa5cf4082565"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the overlay alpha blending value for classes that don't have it explicitly set (between 0-255).  <a href="group__detectNet.html#aefb9214feb29121368dfaa5cf4082565">More...</a><br /></td></tr>
<tr class="separator:aefb9214feb29121368dfaa5cf4082565"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac2ffd50b5fb52d6ceea09af29b144afc"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#ac2ffd50b5fb52d6ceea09af29b144afc">SetOverlayAlpha</a> (float <a class="el" href="cudaVector_8h.html#ac0d98a665e25ffa6d701a2ce2f6efd12">alpha</a>)</td></tr>
<tr class="memdesc:ac2ffd50b5fb52d6ceea09af29b144afc"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set overlay alpha blending value for all classes (between 0-255).  <a href="group__detectNet.html#ac2ffd50b5fb52d6ceea09af29b144afc">More...</a><br /></td></tr>
<tr class="separator:ac2ffd50b5fb52d6ceea09af29b144afc"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_methods_group__tensorNet"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_group__tensorNet')"><img src="closed.png" alt="-"/>&#160;Public Member Functions inherited from <a class="el" href="group__tensorNet.html#classtensorNet">tensorNet</a></td></tr>
<tr class="memitem:ad19aafbfa262f9b8ffb0bff561f4d7f7 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#ad19aafbfa262f9b8ffb0bff561f4d7f7">~tensorNet</a> ()</td></tr>
<tr class="memdesc:ad19aafbfa262f9b8ffb0bff561f4d7f7 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destory.  <a href="group__tensorNet.html#ad19aafbfa262f9b8ffb0bff561f4d7f7">More...</a><br /></td></tr>
<tr class="separator:ad19aafbfa262f9b8ffb0bff561f4d7f7 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2e63d4670461814bd863ee0d9bd41526 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a2e63d4670461814bd863ee0d9bd41526">LoadNetwork</a> (const char *prototxt, const char *model, const char *mean=NULL, const char *input_blob=&quot;data&quot;, const char *output_blob=&quot;prob&quot;, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a2e63d4670461814bd863ee0d9bd41526 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance.  <a href="group__tensorNet.html#a2e63d4670461814bd863ee0d9bd41526">More...</a><br /></td></tr>
<tr class="separator:a2e63d4670461814bd863ee0d9bd41526 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0a06ffd12b465f39160f4a6925cccd9f inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a0a06ffd12b465f39160f4a6925cccd9f">LoadNetwork</a> (const char *prototxt, const char *model, const char *mean, const char *input_blob, const std::vector&lt; std::string &gt; &amp;output_blobs, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a0a06ffd12b465f39160f4a6925cccd9f inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance with multiple output layers.  <a href="group__tensorNet.html#a0a06ffd12b465f39160f4a6925cccd9f">More...</a><br /></td></tr>
<tr class="separator:a0a06ffd12b465f39160f4a6925cccd9f inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a68a6f21680ae91bc51bea376221d1c48 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a68a6f21680ae91bc51bea376221d1c48">LoadNetwork</a> (const char *prototxt, const char *model, const char *mean, const std::vector&lt; std::string &gt; &amp;input_blobs, const std::vector&lt; std::string &gt; &amp;output_blobs, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a68a6f21680ae91bc51bea376221d1c48 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance with multiple input layers.  <a href="group__tensorNet.html#a68a6f21680ae91bc51bea376221d1c48">More...</a><br /></td></tr>
<tr class="separator:a68a6f21680ae91bc51bea376221d1c48 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a168c7f75c9fd6d264afd016e144f3878 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a168c7f75c9fd6d264afd016e144f3878">LoadNetwork</a> (const char *prototxt, const char *model, const char *mean, const char *input_blob, const <a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a> &amp;input_dims, const std::vector&lt; std::string &gt; &amp;output_blobs, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a168c7f75c9fd6d264afd016e144f3878 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance (this variant is used for UFF models)  <a href="group__tensorNet.html#a168c7f75c9fd6d264afd016e144f3878">More...</a><br /></td></tr>
<tr class="separator:a168c7f75c9fd6d264afd016e144f3878 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8f34a6001c2da01662b85670de9246e4 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a8f34a6001c2da01662b85670de9246e4">LoadNetwork</a> (const char *prototxt, const char *model, const char *mean, const std::vector&lt; std::string &gt; &amp;input_blobs, const std::vector&lt; <a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a> &gt; &amp;input_dims, const std::vector&lt; std::string &gt; &amp;output_blobs, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a8f34a6001c2da01662b85670de9246e4 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance with multiple input layers (used for UFF models)  <a href="group__tensorNet.html#a8f34a6001c2da01662b85670de9246e4">More...</a><br /></td></tr>
<tr class="separator:a8f34a6001c2da01662b85670de9246e4 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acb8076f6ab8d13b6507140826cf438d8 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#acb8076f6ab8d13b6507140826cf438d8">LoadEngine</a> (const char *engine_filename, const std::vector&lt; std::string &gt; &amp;input_blobs, const std::vector&lt; std::string &gt; &amp;output_blobs, nvinfer1::IPluginFactory *pluginFactory=NULL, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:acb8076f6ab8d13b6507140826cf438d8 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a network instance from a serialized engine plan file.  <a href="group__tensorNet.html#acb8076f6ab8d13b6507140826cf438d8">More...</a><br /></td></tr>
<tr class="separator:acb8076f6ab8d13b6507140826cf438d8 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aaa4efe2b8d91fe914a22c87b725ac063 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#aaa4efe2b8d91fe914a22c87b725ac063">LoadEngine</a> (char *engine_stream, size_t engine_size, const std::vector&lt; std::string &gt; &amp;input_blobs, const std::vector&lt; std::string &gt; &amp;output_blobs, nvinfer1::IPluginFactory *pluginFactory=NULL, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:aaa4efe2b8d91fe914a22c87b725ac063 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a network instance from a serialized engine plan file.  <a href="group__tensorNet.html#aaa4efe2b8d91fe914a22c87b725ac063">More...</a><br /></td></tr>
<tr class="separator:aaa4efe2b8d91fe914a22c87b725ac063 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2d6fe13696a49d61e9abfa9729153e65 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a2d6fe13696a49d61e9abfa9729153e65">LoadEngine</a> (nvinfer1::ICudaEngine *engine, const std::vector&lt; std::string &gt; &amp;input_blobs, const std::vector&lt; std::string &gt; &amp;output_blobs, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a2d6fe13696a49d61e9abfa9729153e65 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load network resources from an existing TensorRT engine instance.  <a href="group__tensorNet.html#a2d6fe13696a49d61e9abfa9729153e65">More...</a><br /></td></tr>
<tr class="separator:a2d6fe13696a49d61e9abfa9729153e65 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a89755f8e4b72ead7460deed394967386 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a89755f8e4b72ead7460deed394967386">LoadEngine</a> (const char *filename, char **stream, size_t *size)</td></tr>
<tr class="memdesc:a89755f8e4b72ead7460deed394967386 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a serialized engine plan file into memory.  <a href="group__tensorNet.html#a89755f8e4b72ead7460deed394967386">More...</a><br /></td></tr>
<tr class="separator:a89755f8e4b72ead7460deed394967386 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3413eb0ad4f240f457f192f39e2e03e8 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a3413eb0ad4f240f457f192f39e2e03e8">EnableLayerProfiler</a> ()</td></tr>
<tr class="memdesc:a3413eb0ad4f240f457f192f39e2e03e8 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Manually enable layer profiling times.  <a href="group__tensorNet.html#a3413eb0ad4f240f457f192f39e2e03e8">More...</a><br /></td></tr>
<tr class="separator:a3413eb0ad4f240f457f192f39e2e03e8 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae49f74ff83e46112a30318fa0576cace inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#ae49f74ff83e46112a30318fa0576cace">EnableDebug</a> ()</td></tr>
<tr class="memdesc:ae49f74ff83e46112a30318fa0576cace inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Manually enable debug messages and synchronization.  <a href="group__tensorNet.html#ae49f74ff83e46112a30318fa0576cace">More...</a><br /></td></tr>
<tr class="separator:ae49f74ff83e46112a30318fa0576cace inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7d0ec0d8504ac8b26c5ab4a6136599ca inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a7d0ec0d8504ac8b26c5ab4a6136599ca">AllowGPUFallback</a> () const</td></tr>
<tr class="memdesc:a7d0ec0d8504ac8b26c5ab4a6136599ca inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Return true if GPU fallback is enabled.  <a href="group__tensorNet.html#a7d0ec0d8504ac8b26c5ab4a6136599ca">More...</a><br /></td></tr>
<tr class="separator:a7d0ec0d8504ac8b26c5ab4a6136599ca inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a92bb737172d26bda5f67d15346a02514 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a92bb737172d26bda5f67d15346a02514">GetDevice</a> () const</td></tr>
<tr class="memdesc:a92bb737172d26bda5f67d15346a02514 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the device being used for execution.  <a href="group__tensorNet.html#a92bb737172d26bda5f67d15346a02514">More...</a><br /></td></tr>
<tr class="separator:a92bb737172d26bda5f67d15346a02514 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afb38b5f171025e987a00214cc4379ca9 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#afb38b5f171025e987a00214cc4379ca9">GetPrecision</a> () const</td></tr>
<tr class="memdesc:afb38b5f171025e987a00214cc4379ca9 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the type of precision being used.  <a href="group__tensorNet.html#afb38b5f171025e987a00214cc4379ca9">More...</a><br /></td></tr>
<tr class="separator:afb38b5f171025e987a00214cc4379ca9 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6b8e8dba05bc5c677027913d8c64f259 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a6b8e8dba05bc5c677027913d8c64f259">IsPrecision</a> (<a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> type) const</td></tr>
<tr class="memdesc:a6b8e8dba05bc5c677027913d8c64f259 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Check if a particular precision is being used.  <a href="group__tensorNet.html#a6b8e8dba05bc5c677027913d8c64f259">More...</a><br /></td></tr>
<tr class="separator:a6b8e8dba05bc5c677027913d8c64f259 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a34e350ec6185277ac09ae55a79403e62 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">cudaStream_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a34e350ec6185277ac09ae55a79403e62">GetStream</a> () const</td></tr>
<tr class="memdesc:a34e350ec6185277ac09ae55a79403e62 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the stream that the device is operating on.  <a href="group__tensorNet.html#a34e350ec6185277ac09ae55a79403e62">More...</a><br /></td></tr>
<tr class="separator:a34e350ec6185277ac09ae55a79403e62 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a78cecfb7505be0ea59d29041abc85cbb inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">cudaStream_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a78cecfb7505be0ea59d29041abc85cbb">CreateStream</a> (bool nonBlocking=true)</td></tr>
<tr class="memdesc:a78cecfb7505be0ea59d29041abc85cbb inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Create and use a new stream for execution.  <a href="group__tensorNet.html#a78cecfb7505be0ea59d29041abc85cbb">More...</a><br /></td></tr>
<tr class="separator:a78cecfb7505be0ea59d29041abc85cbb inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a679b177784c85bfdba63dcd1008ff633 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a679b177784c85bfdba63dcd1008ff633">SetStream</a> (cudaStream_t stream)</td></tr>
<tr class="memdesc:a679b177784c85bfdba63dcd1008ff633 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the stream that the device is operating on.  <a href="group__tensorNet.html#a679b177784c85bfdba63dcd1008ff633">More...</a><br /></td></tr>
<tr class="separator:a679b177784c85bfdba63dcd1008ff633 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a624881afe27acd2b2fff0f0f75308ea2 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a624881afe27acd2b2fff0f0f75308ea2">GetPrototxtPath</a> () const</td></tr>
<tr class="memdesc:a624881afe27acd2b2fff0f0f75308ea2 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the path to the network prototxt file.  <a href="group__tensorNet.html#a624881afe27acd2b2fff0f0f75308ea2">More...</a><br /></td></tr>
<tr class="separator:a624881afe27acd2b2fff0f0f75308ea2 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac74d7f0571b7782b945ff85fd6894044 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#ac74d7f0571b7782b945ff85fd6894044">GetModelPath</a> () const</td></tr>
<tr class="memdesc:ac74d7f0571b7782b945ff85fd6894044 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the full path to model file, including the filename.  <a href="group__tensorNet.html#ac74d7f0571b7782b945ff85fd6894044">More...</a><br /></td></tr>
<tr class="separator:ac74d7f0571b7782b945ff85fd6894044 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a03252bed041613fc1afb9d3cbb99663d inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a03252bed041613fc1afb9d3cbb99663d">GetModelFilename</a> () const</td></tr>
<tr class="memdesc:a03252bed041613fc1afb9d3cbb99663d inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the filename of the file, excluding the directory.  <a href="group__tensorNet.html#a03252bed041613fc1afb9d3cbb99663d">More...</a><br /></td></tr>
<tr class="separator:a03252bed041613fc1afb9d3cbb99663d inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acfa7f1f01b46f658ffc96f8a002e8d48 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="group__tensorNet.html#ga5d4597e0e7beae7133d542e220528725">modelType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#acfa7f1f01b46f658ffc96f8a002e8d48">GetModelType</a> () const</td></tr>
<tr class="memdesc:acfa7f1f01b46f658ffc96f8a002e8d48 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the format of the network model.  <a href="group__tensorNet.html#acfa7f1f01b46f658ffc96f8a002e8d48">More...</a><br /></td></tr>
<tr class="separator:acfa7f1f01b46f658ffc96f8a002e8d48 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0a09d691ea080bd9734c5782c8fff6fd inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a0a09d691ea080bd9734c5782c8fff6fd">IsModelType</a> (<a class="el" href="group__tensorNet.html#ga5d4597e0e7beae7133d542e220528725">modelType</a> type) const</td></tr>
<tr class="memdesc:a0a09d691ea080bd9734c5782c8fff6fd inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Return true if the model is of the specified format.  <a href="group__tensorNet.html#a0a09d691ea080bd9734c5782c8fff6fd">More...</a><br /></td></tr>
<tr class="separator:a0a09d691ea080bd9734c5782c8fff6fd inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac583b8de1dd64b47338b4a3eb42ac166 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#ac583b8de1dd64b47338b4a3eb42ac166">GetInputLayers</a> () const</td></tr>
<tr class="memdesc:ac583b8de1dd64b47338b4a3eb42ac166 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the number of input layers to the network.  <a href="group__tensorNet.html#ac583b8de1dd64b47338b4a3eb42ac166">More...</a><br /></td></tr>
<tr class="separator:ac583b8de1dd64b47338b4a3eb42ac166 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2dcc770a7215e2e76a8d520a36689e16 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a2dcc770a7215e2e76a8d520a36689e16">GetOutputLayers</a> () const</td></tr>
<tr class="memdesc:a2dcc770a7215e2e76a8d520a36689e16 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the number of output layers to the network.  <a href="group__tensorNet.html#a2dcc770a7215e2e76a8d520a36689e16">More...</a><br /></td></tr>
<tr class="separator:a2dcc770a7215e2e76a8d520a36689e16 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:adcfe61596f291e75a87d36c3771f25df inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#adcfe61596f291e75a87d36c3771f25df">GetInputDims</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:adcfe61596f291e75a87d36c3771f25df inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the dimensions of network input layer.  <a href="group__tensorNet.html#adcfe61596f291e75a87d36c3771f25df">More...</a><br /></td></tr>
<tr class="separator:adcfe61596f291e75a87d36c3771f25df inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2d75ef6f579d1a71ff472bfafd0b7795 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a2d75ef6f579d1a71ff472bfafd0b7795">GetInputWidth</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a2d75ef6f579d1a71ff472bfafd0b7795 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the width of network input layer.  <a href="group__tensorNet.html#a2d75ef6f579d1a71ff472bfafd0b7795">More...</a><br /></td></tr>
<tr class="separator:a2d75ef6f579d1a71ff472bfafd0b7795 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a214a92c41dcdcb58b3cd8496aac0857a inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a214a92c41dcdcb58b3cd8496aac0857a">GetInputHeight</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a214a92c41dcdcb58b3cd8496aac0857a inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the height of network input layer.  <a href="group__tensorNet.html#a214a92c41dcdcb58b3cd8496aac0857a">More...</a><br /></td></tr>
<tr class="separator:a214a92c41dcdcb58b3cd8496aac0857a inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2c80d46f8a01335e77e41023544102c9 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a2c80d46f8a01335e77e41023544102c9">GetInputSize</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a2c80d46f8a01335e77e41023544102c9 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the size (in bytes) of network input layer.  <a href="group__tensorNet.html#a2c80d46f8a01335e77e41023544102c9">More...</a><br /></td></tr>
<tr class="separator:a2c80d46f8a01335e77e41023544102c9 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3a8851513971d11746231d217f57b69f inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">float *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a3a8851513971d11746231d217f57b69f">GetInputPtr</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a3a8851513971d11746231d217f57b69f inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the CUDA pointer to the input layer's memory.  <a href="group__tensorNet.html#a3a8851513971d11746231d217f57b69f">More...</a><br /></td></tr>
<tr class="separator:a3a8851513971d11746231d217f57b69f inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a77703f2a7b59f836c93ae28811e22cb0 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a77703f2a7b59f836c93ae28811e22cb0">GetOutputDims</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a77703f2a7b59f836c93ae28811e22cb0 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the dimensions of network output layer.  <a href="group__tensorNet.html#a77703f2a7b59f836c93ae28811e22cb0">More...</a><br /></td></tr>
<tr class="separator:a77703f2a7b59f836c93ae28811e22cb0 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a09d63a8fd906c99f8158bf9460a83c02 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a09d63a8fd906c99f8158bf9460a83c02">GetOutputWidth</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a09d63a8fd906c99f8158bf9460a83c02 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the width of network output layer.  <a href="group__tensorNet.html#a09d63a8fd906c99f8158bf9460a83c02">More...</a><br /></td></tr>
<tr class="separator:a09d63a8fd906c99f8158bf9460a83c02 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a613679e8ee5315f3b5b16a39011ba76e inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a613679e8ee5315f3b5b16a39011ba76e">GetOutputHeight</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a613679e8ee5315f3b5b16a39011ba76e inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the height of network output layer.  <a href="group__tensorNet.html#a613679e8ee5315f3b5b16a39011ba76e">More...</a><br /></td></tr>
<tr class="separator:a613679e8ee5315f3b5b16a39011ba76e inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae1486438dcdbe0d7f5e88e5336a42efa inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#ae1486438dcdbe0d7f5e88e5336a42efa">GetOutputSize</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:ae1486438dcdbe0d7f5e88e5336a42efa inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the size (in bytes) of network output layer.  <a href="group__tensorNet.html#ae1486438dcdbe0d7f5e88e5336a42efa">More...</a><br /></td></tr>
<tr class="separator:ae1486438dcdbe0d7f5e88e5336a42efa inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2e5a4207d90828c31255846b11a431ea inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">float *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a2e5a4207d90828c31255846b11a431ea">GetOutputPtr</a> (uint32_t layer=0) const</td></tr>
<tr class="memdesc:a2e5a4207d90828c31255846b11a431ea inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the CUDA pointer to the output memory.  <a href="group__tensorNet.html#a2e5a4207d90828c31255846b11a431ea">More...</a><br /></td></tr>
<tr class="separator:a2e5a4207d90828c31255846b11a431ea inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9dd2db089176ae6878e9ea7dd8fd80c3 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a9dd2db089176ae6878e9ea7dd8fd80c3">GetNetworkFPS</a> ()</td></tr>
<tr class="memdesc:a9dd2db089176ae6878e9ea7dd8fd80c3 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the network frames per second (FPS).  <a href="group__tensorNet.html#a9dd2db089176ae6878e9ea7dd8fd80c3">More...</a><br /></td></tr>
<tr class="separator:a9dd2db089176ae6878e9ea7dd8fd80c3 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a49faef5920860345e503023b7c84423c inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a49faef5920860345e503023b7c84423c">GetNetworkTime</a> ()</td></tr>
<tr class="memdesc:a49faef5920860345e503023b7c84423c inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the network runtime (in milliseconds).  <a href="group__tensorNet.html#a49faef5920860345e503023b7c84423c">More...</a><br /></td></tr>
<tr class="separator:a49faef5920860345e503023b7c84423c inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ade7badd98d5790b5a58863d56e61e041 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#ade7badd98d5790b5a58863d56e61e041">GetNetworkName</a> () const</td></tr>
<tr class="memdesc:ade7badd98d5790b5a58863d56e61e041 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the network name (it's filename).  <a href="group__tensorNet.html#ade7badd98d5790b5a58863d56e61e041">More...</a><br /></td></tr>
<tr class="separator:ade7badd98d5790b5a58863d56e61e041 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad266f93035a80dca80cd84d971e4f69b inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">float2&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#ad266f93035a80dca80cd84d971e4f69b">GetProfilerTime</a> (<a class="el" href="group__tensorNet.html#gae34d45c0faa674ef4cc0fbfc8fae5809">profilerQuery</a> query)</td></tr>
<tr class="memdesc:ad266f93035a80dca80cd84d971e4f69b inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the profiler runtime (in milliseconds).  <a href="group__tensorNet.html#ad266f93035a80dca80cd84d971e4f69b">More...</a><br /></td></tr>
<tr class="separator:ad266f93035a80dca80cd84d971e4f69b inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a27cf81b3fecf93d2e63a61220a54b393 inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a27cf81b3fecf93d2e63a61220a54b393">GetProfilerTime</a> (<a class="el" href="group__tensorNet.html#gae34d45c0faa674ef4cc0fbfc8fae5809">profilerQuery</a> query, <a class="el" href="group__tensorNet.html#gaaa4127ed22c7165a32d0474ebf97975e">profilerDevice</a> device)</td></tr>
<tr class="memdesc:a27cf81b3fecf93d2e63a61220a54b393 inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the profiler runtime (in milliseconds).  <a href="group__tensorNet.html#a27cf81b3fecf93d2e63a61220a54b393">More...</a><br /></td></tr>
<tr class="separator:a27cf81b3fecf93d2e63a61220a54b393 inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afc0f50abcf6ac71e96d51eba3ed53d4b inherit pub_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#afc0f50abcf6ac71e96d51eba3ed53d4b">PrintProfilerTimes</a> ()</td></tr>
<tr class="memdesc:afc0f50abcf6ac71e96d51eba3ed53d4b inherit pub_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Print the profiler times (in millseconds).  <a href="group__tensorNet.html#afc0f50abcf6ac71e96d51eba3ed53d4b">More...</a><br /></td></tr>
<tr class="separator:afc0f50abcf6ac71e96d51eba3ed53d4b inherit pub_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr><td colspan="2"><h3>Static Public Member Functions</h3></td></tr>
<tr class="memitem:a2bb2b446e26a466c9e355f1472f68e4c"><td class="memItemLeft" align="right" valign="top">static uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a2bb2b446e26a466c9e355f1472f68e4c">OverlayFlagsFromStr</a> (const char *flags)</td></tr>
<tr class="memdesc:a2bb2b446e26a466c9e355f1472f68e4c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Parse a string sequence into OverlayFlags enum.  <a href="group__detectNet.html#a2bb2b446e26a466c9e355f1472f68e4c">More...</a><br /></td></tr>
<tr class="separator:a2bb2b446e26a466c9e355f1472f68e4c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a468b5e636096159500f3db327c31e8dd"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="group__detectNet.html#classdetectNet">detectNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a468b5e636096159500f3db327c31e8dd">Create</a> (const char *network=&quot;ssd-mobilenet-v2&quot;, float threshold=<a class="el" href="group__detectNet.html#ga56785ae0440a9e281a6dc44dc01039a5">DETECTNET_DEFAULT_CONFIDENCE_THRESHOLD</a>, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true)</td></tr>
<tr class="memdesc:a468b5e636096159500f3db327c31e8dd"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a pre-trained model.  <a href="group__detectNet.html#a468b5e636096159500f3db327c31e8dd">More...</a><br /></td></tr>
<tr class="separator:a468b5e636096159500f3db327c31e8dd"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aaf5326a80477f19ecc2064ae0a95c2a5"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="group__detectNet.html#classdetectNet">detectNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#aaf5326a80477f19ecc2064ae0a95c2a5">Create</a> (const char *prototxt_path, const char *model_path, float mean_pixel=0.0f, const char *class_labels=NULL, float threshold=<a class="el" href="group__detectNet.html#ga56785ae0440a9e281a6dc44dc01039a5">DETECTNET_DEFAULT_CONFIDENCE_THRESHOLD</a>, const char *input=<a class="el" href="group__detectNet.html#gac824e329015dc8aed6e1112bfe21cb97">DETECTNET_DEFAULT_INPUT</a>, const char *coverage=<a class="el" href="group__detectNet.html#ga1e79603783719e4a79f2c68f1ef47621">DETECTNET_DEFAULT_COVERAGE</a>, const char *bboxes=<a class="el" href="group__detectNet.html#gac8c52a38ffa041865ed71fd2ea806620">DETECTNET_DEFAULT_BBOX</a>, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true)</td></tr>
<tr class="memdesc:aaf5326a80477f19ecc2064ae0a95c2a5"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a custom network instance.  <a href="group__detectNet.html#aaf5326a80477f19ecc2064ae0a95c2a5">More...</a><br /></td></tr>
<tr class="separator:aaf5326a80477f19ecc2064ae0a95c2a5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9981735c38d2cb97205aa9e255ab4a0e"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="group__detectNet.html#classdetectNet">detectNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a9981735c38d2cb97205aa9e255ab4a0e">Create</a> (const char *prototxt_path, const char *model_path, float mean_pixel, const char *class_labels, const char *class_colors, float threshold=<a class="el" href="group__detectNet.html#ga56785ae0440a9e281a6dc44dc01039a5">DETECTNET_DEFAULT_CONFIDENCE_THRESHOLD</a>, const char *input=<a class="el" href="group__detectNet.html#gac824e329015dc8aed6e1112bfe21cb97">DETECTNET_DEFAULT_INPUT</a>, const char *coverage=<a class="el" href="group__detectNet.html#ga1e79603783719e4a79f2c68f1ef47621">DETECTNET_DEFAULT_COVERAGE</a>, const char *bboxes=<a class="el" href="group__detectNet.html#gac8c52a38ffa041865ed71fd2ea806620">DETECTNET_DEFAULT_BBOX</a>, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true)</td></tr>
<tr class="memdesc:a9981735c38d2cb97205aa9e255ab4a0e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a custom network instance.  <a href="group__detectNet.html#a9981735c38d2cb97205aa9e255ab4a0e">More...</a><br /></td></tr>
<tr class="separator:a9981735c38d2cb97205aa9e255ab4a0e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a761950971444cd0970143b3607c5cb06"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="group__detectNet.html#classdetectNet">detectNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a761950971444cd0970143b3607c5cb06">Create</a> (const char *model_path, const char *class_labels, float threshold, const char *input, const <a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a> &amp;inputDims, const char *output, const char *numDetections, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true)</td></tr>
<tr class="memdesc:a761950971444cd0970143b3607c5cb06"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a custom network instance of a UFF model.  <a href="group__detectNet.html#a761950971444cd0970143b3607c5cb06">More...</a><br /></td></tr>
<tr class="separator:a761950971444cd0970143b3607c5cb06"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aeca3a465ca3fa41d8e184025c0cbbd8c"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="group__detectNet.html#classdetectNet">detectNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#aeca3a465ca3fa41d8e184025c0cbbd8c">Create</a> (int argc, char **argv)</td></tr>
<tr class="memdesc:aeca3a465ca3fa41d8e184025c0cbbd8c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance by parsing the command line.  <a href="group__detectNet.html#aeca3a465ca3fa41d8e184025c0cbbd8c">More...</a><br /></td></tr>
<tr class="separator:aeca3a465ca3fa41d8e184025c0cbbd8c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0543ed1697d294654ebfea175f1431e3"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="group__detectNet.html#classdetectNet">detectNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a0543ed1697d294654ebfea175f1431e3">Create</a> (const <a class="el" href="group__commandLine.html#classcommandLine">commandLine</a> &amp;cmdLine)</td></tr>
<tr class="memdesc:a0543ed1697d294654ebfea175f1431e3"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance by parsing the command line.  <a href="group__detectNet.html#a0543ed1697d294654ebfea175f1431e3">More...</a><br /></td></tr>
<tr class="separator:a0543ed1697d294654ebfea175f1431e3"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8c336e6b018a780e7ca74897d1bce628"><td class="memItemLeft" align="right" valign="top">static const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a8c336e6b018a780e7ca74897d1bce628">Usage</a> ()</td></tr>
<tr class="memdesc:a8c336e6b018a780e7ca74897d1bce628"><td class="mdescLeft">&#160;</td><td class="mdescRight">Usage string for command line arguments to <a class="el" href="group__detectNet.html#a468b5e636096159500f3db327c31e8dd" title="Load a pre-trained model.">Create()</a>  <a href="group__detectNet.html#a8c336e6b018a780e7ca74897d1bce628">More...</a><br /></td></tr>
<tr class="separator:a8c336e6b018a780e7ca74897d1bce628"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_static_methods_group__tensorNet"><td colspan="2" onclick="javascript:toggleInherit('pub_static_methods_group__tensorNet')"><img src="closed.png" alt="-"/>&#160;Static Public Member Functions inherited from <a class="el" href="group__tensorNet.html#classtensorNet">tensorNet</a></td></tr>
<tr class="memitem:a57cacfea82e9329c2cf776837dd00aef inherit pub_static_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a57cacfea82e9329c2cf776837dd00aef">LoadClassLabels</a> (const char *filename, std::vector&lt; std::string &gt; &amp;descriptions, int expectedClasses=-1)</td></tr>
<tr class="memdesc:a57cacfea82e9329c2cf776837dd00aef inherit pub_static_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load class descriptions from a label file.  <a href="group__tensorNet.html#a57cacfea82e9329c2cf776837dd00aef">More...</a><br /></td></tr>
<tr class="separator:a57cacfea82e9329c2cf776837dd00aef inherit pub_static_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa92022958d3a46655a5e2f2ed416e6b5 inherit pub_static_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#aa92022958d3a46655a5e2f2ed416e6b5">LoadClassLabels</a> (const char *filename, std::vector&lt; std::string &gt; &amp;descriptions, std::vector&lt; std::string &gt; &amp;synsets, int expectedClasses=-1)</td></tr>
<tr class="memdesc:aa92022958d3a46655a5e2f2ed416e6b5 inherit pub_static_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load class descriptions and synset strings from a label file.  <a href="group__tensorNet.html#aa92022958d3a46655a5e2f2ed416e6b5">More...</a><br /></td></tr>
<tr class="separator:aa92022958d3a46655a5e2f2ed416e6b5 inherit pub_static_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7b87410f9133aea37b46979d543219b9 inherit pub_static_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a7b87410f9133aea37b46979d543219b9">LoadClassColors</a> (const char *filename, float4 *colors, int expectedClasses, float defaultAlpha=255.0f)</td></tr>
<tr class="memdesc:a7b87410f9133aea37b46979d543219b9 inherit pub_static_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load class colors from a text file.  <a href="group__tensorNet.html#a7b87410f9133aea37b46979d543219b9">More...</a><br /></td></tr>
<tr class="separator:a7b87410f9133aea37b46979d543219b9 inherit pub_static_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae5dd58e2481f6c703abb9abbcfce805e inherit pub_static_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#ae5dd58e2481f6c703abb9abbcfce805e">LoadClassColors</a> (const char *filename, float4 **colors, int expectedClasses, float defaultAlpha=255.0f)</td></tr>
<tr class="memdesc:ae5dd58e2481f6c703abb9abbcfce805e inherit pub_static_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load class colors from a text file.  <a href="group__tensorNet.html#ae5dd58e2481f6c703abb9abbcfce805e">More...</a><br /></td></tr>
<tr class="separator:ae5dd58e2481f6c703abb9abbcfce805e inherit pub_static_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a4fe18908c74efda1708029ca3b04f0e8 inherit pub_static_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">static float4&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a4fe18908c74efda1708029ca3b04f0e8">GenerateColor</a> (uint32_t <a class="el" href="cudaPointCloud_8h.html#ad9bd89745d72dbc52651f62814eed36d">classID</a>, float <a class="el" href="cudaVector_8h.html#ac0d98a665e25ffa6d701a2ce2f6efd12">alpha</a>=255.0f)</td></tr>
<tr class="memdesc:a4fe18908c74efda1708029ca3b04f0e8 inherit pub_static_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Procedurally generate a color for a given class index with the specified alpha value.  <a href="group__tensorNet.html#a4fe18908c74efda1708029ca3b04f0e8">More...</a><br /></td></tr>
<tr class="separator:a4fe18908c74efda1708029ca3b04f0e8 inherit pub_static_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3c0509631176be6f9e25673cb0aa12dc inherit pub_static_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a3c0509631176be6f9e25673cb0aa12dc">SelectPrecision</a> (<a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowInt8=true)</td></tr>
<tr class="memdesc:a3c0509631176be6f9e25673cb0aa12dc inherit pub_static_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Resolve a desired precision to a specific one that's available.  <a href="group__tensorNet.html#a3c0509631176be6f9e25673cb0aa12dc">More...</a><br /></td></tr>
<tr class="separator:a3c0509631176be6f9e25673cb0aa12dc inherit pub_static_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:abe33fae5332296e2d917cb4ce435e255 inherit pub_static_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#abe33fae5332296e2d917cb4ce435e255">FindFastestPrecision</a> (<a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowInt8=true)</td></tr>
<tr class="memdesc:abe33fae5332296e2d917cb4ce435e255 inherit pub_static_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Determine the fastest native precision on a device.  <a href="group__tensorNet.html#abe33fae5332296e2d917cb4ce435e255">More...</a><br /></td></tr>
<tr class="separator:abe33fae5332296e2d917cb4ce435e255 inherit pub_static_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae88436e652afdd7bceef7cb7c5fde7a6 inherit pub_static_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">static std::vector&lt; <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#ae88436e652afdd7bceef7cb7c5fde7a6">DetectNativePrecisions</a> (<a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>)</td></tr>
<tr class="memdesc:ae88436e652afdd7bceef7cb7c5fde7a6 inherit pub_static_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect the precisions supported natively on a device.  <a href="group__tensorNet.html#ae88436e652afdd7bceef7cb7c5fde7a6">More...</a><br /></td></tr>
<tr class="separator:ae88436e652afdd7bceef7cb7c5fde7a6 inherit pub_static_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa3bf1a3bf1fca38b39a200b4d8f727b2 inherit pub_static_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#aa3bf1a3bf1fca38b39a200b4d8f727b2">DetectNativePrecision</a> (const std::vector&lt; <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> &gt; &amp;nativeTypes, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> type)</td></tr>
<tr class="memdesc:aa3bf1a3bf1fca38b39a200b4d8f727b2 inherit pub_static_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect if a particular precision is supported natively.  <a href="group__tensorNet.html#aa3bf1a3bf1fca38b39a200b4d8f727b2">More...</a><br /></td></tr>
<tr class="separator:aa3bf1a3bf1fca38b39a200b4d8f727b2 inherit pub_static_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7d72ec8bbaf61278ce533afd60d5391c inherit pub_static_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a7d72ec8bbaf61278ce533afd60d5391c">DetectNativePrecision</a> (<a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>)</td></tr>
<tr class="memdesc:a7d72ec8bbaf61278ce533afd60d5391c inherit pub_static_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect if a particular precision is supported natively.  <a href="group__tensorNet.html#a7d72ec8bbaf61278ce533afd60d5391c">More...</a><br /></td></tr>
<tr class="separator:a7d72ec8bbaf61278ce533afd60d5391c inherit pub_static_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr><td colspan="2"><h3>Protected Member Functions</h3></td></tr>
<tr class="memitem:a07a419d97f14aa0962b737e671ff438a"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a07a419d97f14aa0962b737e671ff438a">detectNet</a> (float meanPixel=0.0f)</td></tr>
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<tr class="memitem:acb367fb540a1dfb719c44ae0e4cdb045"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#acb367fb540a1dfb719c44ae0e4cdb045">allocDetections</a> ()</td></tr>
<tr class="separator:acb367fb540a1dfb719c44ae0e4cdb045"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a5ab26d1cfb1710a826cae4473c7173f5"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a5ab26d1cfb1710a826cae4473c7173f5">loadClassInfo</a> (const char *filename)</td></tr>
<tr class="separator:a5ab26d1cfb1710a826cae4473c7173f5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2c649836482b3bfe12f4ab671387aa77"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a2c649836482b3bfe12f4ab671387aa77">loadClassColors</a> (const char *filename)</td></tr>
<tr class="separator:a2c649836482b3bfe12f4ab671387aa77"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a77af699123da152d3e83ca355ed4de11"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a77af699123da152d3e83ca355ed4de11">init</a> (const char *prototxt_path, const char *model_path, const char *class_labels, const char *class_colors, float threshold, const char *input, const char *coverage, const char *bboxes, uint32_t maxBatchSize, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device, bool allowGPUFallback)</td></tr>
<tr class="separator:a77af699123da152d3e83ca355ed4de11"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a54aa7f77769d35fbd92d205904d364a2"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a54aa7f77769d35fbd92d205904d364a2">preProcess</a> (void *input, uint32_t width, uint32_t height, <a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a> format)</td></tr>
<tr class="separator:a54aa7f77769d35fbd92d205904d364a2"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a283969d1290eb6675356b7a96ecb89e6"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a283969d1290eb6675356b7a96ecb89e6">postProcess</a> (void *input, uint32_t width, uint32_t height, <a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a> format, <a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *detections)</td></tr>
<tr class="separator:a283969d1290eb6675356b7a96ecb89e6"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3692a43f224a5b9178203a3e6c6d7b51"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a3692a43f224a5b9178203a3e6c6d7b51">postProcessSSD_UFF</a> (<a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *detections, uint32_t width, uint32_t height)</td></tr>
<tr class="separator:a3692a43f224a5b9178203a3e6c6d7b51"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<tr class="memitem:ad72b6e8d9c429fd2d4a9a58b829cae90"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#ad72b6e8d9c429fd2d4a9a58b829cae90">clusterDetections</a> (<a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *detections, int n)</td></tr>
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<tr class="memitem:ae25ca7f51a3235011442ac1edeafe1d8"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#ae25ca7f51a3235011442ac1edeafe1d8">sortDetections</a> (<a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *detections, int numDetections)</td></tr>
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<tr class="inherit_header pro_methods_group__tensorNet"><td colspan="2" onclick="javascript:toggleInherit('pro_methods_group__tensorNet')"><img src="closed.png" alt="-"/>&#160;Protected Member Functions inherited from <a class="el" href="group__tensorNet.html#classtensorNet">tensorNet</a></td></tr>
<tr class="memitem:ab6e617d96e5542bef023ee9d4c96388a inherit pro_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#ab6e617d96e5542bef023ee9d4c96388a">tensorNet</a> ()</td></tr>
<tr class="memdesc:ab6e617d96e5542bef023ee9d4c96388a inherit pro_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor.  <a href="group__tensorNet.html#ab6e617d96e5542bef023ee9d4c96388a">More...</a><br /></td></tr>
<tr class="separator:ab6e617d96e5542bef023ee9d4c96388a inherit pro_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2e8dd909e797dfcfbb058dc6b351c586 inherit pro_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a2e8dd909e797dfcfbb058dc6b351c586">ProcessNetwork</a> (bool sync=true)</td></tr>
<tr class="memdesc:a2e8dd909e797dfcfbb058dc6b351c586 inherit pro_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Execute processing of the network.  <a href="group__tensorNet.html#a2e8dd909e797dfcfbb058dc6b351c586">More...</a><br /></td></tr>
<tr class="separator:a2e8dd909e797dfcfbb058dc6b351c586 inherit pro_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2fbc013f70b52f885867302446e0dca1 inherit pro_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a2fbc013f70b52f885867302446e0dca1">ProfileModel</a> (const std::string &amp;deployFile, const std::string &amp;modelFile, const std::vector&lt; std::string &gt; &amp;inputs, const std::vector&lt; <a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a> &gt; &amp;inputDims, const std::vector&lt; std::string &gt; &amp;outputs, uint32_t maxBatchSize, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device, bool allowGPUFallback, nvinfer1::IInt8Calibrator *calibrator, char **engineStream, size_t *engineSize)</td></tr>
<tr class="memdesc:a2fbc013f70b52f885867302446e0dca1 inherit pro_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Create and output an optimized network model.  <a href="group__tensorNet.html#a2fbc013f70b52f885867302446e0dca1">More...</a><br /></td></tr>
<tr class="separator:a2fbc013f70b52f885867302446e0dca1 inherit pro_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7a898dfb2553869cdc318ecb03e153f1 inherit pro_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a7a898dfb2553869cdc318ecb03e153f1">ConfigureBuilder</a> (nvinfer1::IBuilder *builder, uint32_t maxBatchSize, uint32_t workspaceSize, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device, bool allowGPUFallback, nvinfer1::IInt8Calibrator *calibrator)</td></tr>
<tr class="memdesc:a7a898dfb2553869cdc318ecb03e153f1 inherit pro_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Configure builder options.  <a href="group__tensorNet.html#a7a898dfb2553869cdc318ecb03e153f1">More...</a><br /></td></tr>
<tr class="separator:a7a898dfb2553869cdc318ecb03e153f1 inherit pro_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6e2fe0a467929d76b20940771b8f96c3 inherit pro_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a6e2fe0a467929d76b20940771b8f96c3">ValidateEngine</a> (const char *model_path, const char *cache_path, const char *checksum_path)</td></tr>
<tr class="memdesc:a6e2fe0a467929d76b20940771b8f96c3 inherit pro_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Validate that the model already has a built TensorRT engine that exists and doesn't need updating.  <a href="group__tensorNet.html#a6e2fe0a467929d76b20940771b8f96c3">More...</a><br /></td></tr>
<tr class="separator:a6e2fe0a467929d76b20940771b8f96c3 inherit pro_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a088c3bf591e45e52ec227491f6f299ad inherit pro_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a088c3bf591e45e52ec227491f6f299ad">PROFILER_BEGIN</a> (<a class="el" href="group__tensorNet.html#gae34d45c0faa674ef4cc0fbfc8fae5809">profilerQuery</a> query)</td></tr>
<tr class="memdesc:a088c3bf591e45e52ec227491f6f299ad inherit pro_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Begin a profiling query, before network is run.  <a href="group__tensorNet.html#a088c3bf591e45e52ec227491f6f299ad">More...</a><br /></td></tr>
<tr class="separator:a088c3bf591e45e52ec227491f6f299ad inherit pro_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac8582b9a6099e3265da4c3f9fdf804ea inherit pro_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#ac8582b9a6099e3265da4c3f9fdf804ea">PROFILER_END</a> (<a class="el" href="group__tensorNet.html#gae34d45c0faa674ef4cc0fbfc8fae5809">profilerQuery</a> query)</td></tr>
<tr class="memdesc:ac8582b9a6099e3265da4c3f9fdf804ea inherit pro_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">End a profiling query, after the network is run.  <a href="group__tensorNet.html#ac8582b9a6099e3265da4c3f9fdf804ea">More...</a><br /></td></tr>
<tr class="separator:ac8582b9a6099e3265da4c3f9fdf804ea inherit pro_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae2e0ae17baf6e1975aaad7a7f5c60ce9 inherit pro_methods_group__tensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#ae2e0ae17baf6e1975aaad7a7f5c60ce9">PROFILER_QUERY</a> (<a class="el" href="group__tensorNet.html#gae34d45c0faa674ef4cc0fbfc8fae5809">profilerQuery</a> query)</td></tr>
<tr class="memdesc:ae2e0ae17baf6e1975aaad7a7f5c60ce9 inherit pro_methods_group__tensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Query the CUDA part of a profiler query.  <a href="group__tensorNet.html#ae2e0ae17baf6e1975aaad7a7f5c60ce9">More...</a><br /></td></tr>
<tr class="separator:ae2e0ae17baf6e1975aaad7a7f5c60ce9 inherit pro_methods_group__tensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr><td colspan="2"><h3>Protected Attributes</h3></td></tr>
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<tr class="separator:a70f7dc6392ad89b7f61339830d145577"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afcd4d30a1750b48924ec837c2ddb6cca"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#afcd4d30a1750b48924ec837c2ddb6cca">mConfidenceThreshold</a></td></tr>
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<tr class="memitem:a57cdfaea245446565f4e6f72edb0f857"><td class="memItemLeft" align="right" valign="top">std::vector&lt; std::string &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a57cdfaea245446565f4e6f72edb0f857">mClassDesc</a></td></tr>
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<tr class="memitem:a1c85b56e7b1e4b3e474f47c7f18a157d"><td class="memItemLeft" align="right" valign="top">std::vector&lt; std::string &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a1c85b56e7b1e4b3e474f47c7f18a157d">mClassSynset</a></td></tr>
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<tr class="memitem:ab2da21dcd11e7e91054194b0c87332d9"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#ab2da21dcd11e7e91054194b0c87332d9">mDetectionSets</a></td></tr>
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<tr class="inherit_header pro_attribs_group__tensorNet"><td colspan="2" onclick="javascript:toggleInherit('pro_attribs_group__tensorNet')"><img src="closed.png" alt="-"/>&#160;Protected Attributes inherited from <a class="el" href="group__tensorNet.html#classtensorNet">tensorNet</a></td></tr>
<tr class="memitem:a0c6f7cc68ce87e0701029d40b46d1b81 inherit pro_attribs_group__tensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtensorNet_1_1Logger.html">tensorNet::Logger</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__tensorNet.html#a0c6f7cc68ce87e0701029d40b46d1b81">gLogger</a></td></tr>
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<tr><td colspan="2"><h3>Static Protected Attributes</h3></td></tr>
<tr class="memitem:a77b8f8059780ab552d97a046c32e1f8a"><td class="memItemLeft" align="right" valign="top">static const uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__detectNet.html#a77b8f8059780ab552d97a046c32e1f8a">mNumDetectionSets</a> = 16</td></tr>
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<h4 class="groupheader">Member Enumeration Documentation</h4>
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<h2 class="memtitle"><span class="permalink"><a href="#a29e74cde23a8dd541dbd848e457663d6">&#9670;&nbsp;</a></span>OverlayFlags</h2>

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<p>Overlay flags (can be OR'd together). </p>
<table class="fieldtable">
<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="a29e74cde23a8dd541dbd848e457663d6a966eaaaa69ed08d36dcb862e3fc644bf"></a>OVERLAY_NONE&#160;</td><td class="fielddoc"><p>No overlay. </p>
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<tr><td class="fieldname"><a id="a29e74cde23a8dd541dbd848e457663d6aff23b9098d5bf0f91c31954001c2ff68"></a>OVERLAY_BOX&#160;</td><td class="fielddoc"><p>Overlay the object bounding boxes (filled) </p>
</td></tr>
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<tr><td class="fieldname"><a id="a29e74cde23a8dd541dbd848e457663d6af7ce69cd21d84fb3b963713da66756eb"></a>OVERLAY_CONFIDENCE&#160;</td><td class="fielddoc"><p>Overlay the detection confidence values. </p>
</td></tr>
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</td></tr>
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</td></tr>
</table>

</div>
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<h4 class="groupheader">Constructor &amp; Destructor Documentation</h4>
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<h2 class="memtitle"><span class="permalink"><a href="#a82c0e177e0da142fa22aa7580077307d">&#9670;&nbsp;</a></span>~detectNet()</h2>

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<p>Destory. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a07a419d97f14aa0962b737e671ff438a">&#9670;&nbsp;</a></span>detectNet()</h2>

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<h4 class="groupheader">Member Function Documentation</h4>
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<h2 class="memtitle"><span class="permalink"><a href="#acb367fb540a1dfb719c44ae0e4cdb045">&#9670;&nbsp;</a></span>allocDetections()</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#ad72b6e8d9c429fd2d4a9a58b829cae90">&#9670;&nbsp;</a></span>clusterDetections()</h2>

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          <td class="paramkey"></td>
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          <td class="paramtype">int&#160;</td>
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          <td></td>
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<h2 class="memtitle"><span class="permalink"><a href="#a761950971444cd0970143b3607c5cb06">&#9670;&nbsp;</a></span>Create() <span class="overload">[1/6]</span></h2>

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          <td class="memname">static <a class="el" href="group__detectNet.html#classdetectNet">detectNet</a>* detectNet::Create </td>
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          <td class="paramtype">float&#160;</td>
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          <td class="paramtype">const char *&#160;</td>
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          <td class="paramtype">const char *&#160;</td>
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          <td class="paramkey"></td>
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<p>Load a custom network instance of a UFF model. </p>
<dl class="params"><dt>Parameters</dt><dd>
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    <tr><td class="paramname">model_path</td><td>File path to the UFF model </td></tr>
    <tr><td class="paramname">class_labels</td><td>File path to list of class name labels </td></tr>
    <tr><td class="paramname">threshold</td><td>default minimum threshold for detection </td></tr>
    <tr><td class="paramname">input</td><td>Name of the input layer blob. </td></tr>
    <tr><td class="paramname">inputDims</td><td>Dimensions of the input layer blob. </td></tr>
    <tr><td class="paramname">output</td><td>Name of the output layer blob containing the bounding boxes, ect. </td></tr>
    <tr><td class="paramname">numDetections</td><td>Name of the output layer blob containing the detection count. </td></tr>
    <tr><td class="paramname">maxBatchSize</td><td>The maximum batch size that the network will support and be optimized for. </td></tr>
  </table>
  </dd>
</dl>

</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a468b5e636096159500f3db327c31e8dd">&#9670;&nbsp;</a></span>Create() <span class="overload">[2/6]</span></h2>

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          <td class="memname">static <a class="el" href="group__detectNet.html#classdetectNet">detectNet</a>* detectNet::Create </td>
          <td>(</td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>network</em> = <code>&quot;ssd-mobilenet-v2&quot;</code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>threshold</em> = <code><a class="el" href="group__detectNet.html#ga56785ae0440a9e281a6dc44dc01039a5">DETECTNET_DEFAULT_CONFIDENCE_THRESHOLD</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>maxBatchSize</em> = <code><a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td>
          <td class="paramname"><em>precision</em> = <code><a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a>&#160;</td>
          <td class="paramname"><em>device</em> = <code><a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>allowGPUFallback</em> = <code>true</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
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<span class="mlabels"><span class="mlabel">static</span></span>  </td>
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<p>Load a pre-trained model. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">network</td><td>the pre-trained model to load (</td></tr>
  </table>
  </dd>
</dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="group__detectNet.html#gad5f43885a04689f10c6f9d297ab88a8d" title="Standard command-line options able to be passed to detectNet::Create()">DETECTNET_USAGE_STRING</a> for models) </dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">threshold</td><td>default minimum threshold for detection </td></tr>
    <tr><td class="paramname">maxBatchSize</td><td>The maximum batch size that the network will support and be optimized for. </td></tr>
  </table>
  </dd>
</dl>

</div>
</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a9981735c38d2cb97205aa9e255ab4a0e">&#9670;&nbsp;</a></span>Create() <span class="overload">[3/6]</span></h2>

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          <td class="memname">static <a class="el" href="group__detectNet.html#classdetectNet">detectNet</a>* detectNet::Create </td>
          <td>(</td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>prototxt_path</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>model_path</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>mean_pixel</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>class_labels</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>class_colors</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>threshold</em> = <code><a class="el" href="group__detectNet.html#ga56785ae0440a9e281a6dc44dc01039a5">DETECTNET_DEFAULT_CONFIDENCE_THRESHOLD</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>input</em> = <code><a class="el" href="group__detectNet.html#gac824e329015dc8aed6e1112bfe21cb97">DETECTNET_DEFAULT_INPUT</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>coverage</em> = <code><a class="el" href="group__detectNet.html#ga1e79603783719e4a79f2c68f1ef47621">DETECTNET_DEFAULT_COVERAGE</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>bboxes</em> = <code><a class="el" href="group__detectNet.html#gac8c52a38ffa041865ed71fd2ea806620">DETECTNET_DEFAULT_BBOX</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>maxBatchSize</em> = <code><a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td>
          <td class="paramname"><em>precision</em> = <code><a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a>&#160;</td>
          <td class="paramname"><em>device</em> = <code><a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>allowGPUFallback</em> = <code>true</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
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<span class="mlabels"><span class="mlabel">static</span></span>  </td>
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<p>Load a custom network instance. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">prototxt_path</td><td>File path to the deployable network prototxt </td></tr>
    <tr><td class="paramname">model_path</td><td>File path to the caffemodel </td></tr>
    <tr><td class="paramname">mean_pixel</td><td>Input transform subtraction value (use 0.0 if the network already does this) </td></tr>
    <tr><td class="paramname">class_labels</td><td>File path to list of class name labels </td></tr>
    <tr><td class="paramname">class_colors</td><td>File path to list of class colors </td></tr>
    <tr><td class="paramname">threshold</td><td>default minimum threshold for detection </td></tr>
    <tr><td class="paramname">input</td><td>Name of the input layer blob. </td></tr>
    <tr><td class="paramname">coverage</td><td>Name of the output coverage classifier layer blob, which contains the confidence values for each bbox. </td></tr>
    <tr><td class="paramname">bboxes</td><td>Name of the output bounding box layer blob, which contains a grid of rectangles in the image. </td></tr>
    <tr><td class="paramname">maxBatchSize</td><td>The maximum batch size that the network will support and be optimized for. </td></tr>
  </table>
  </dd>
</dl>

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<h2 class="memtitle"><span class="permalink"><a href="#aaf5326a80477f19ecc2064ae0a95c2a5">&#9670;&nbsp;</a></span>Create() <span class="overload">[4/6]</span></h2>

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          <td class="memname">static <a class="el" href="group__detectNet.html#classdetectNet">detectNet</a>* detectNet::Create </td>
          <td>(</td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>prototxt_path</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>model_path</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>mean_pixel</em> = <code>0.0f</code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>class_labels</em> = <code>NULL</code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>threshold</em> = <code><a class="el" href="group__detectNet.html#ga56785ae0440a9e281a6dc44dc01039a5">DETECTNET_DEFAULT_CONFIDENCE_THRESHOLD</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>input</em> = <code><a class="el" href="group__detectNet.html#gac824e329015dc8aed6e1112bfe21cb97">DETECTNET_DEFAULT_INPUT</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>coverage</em> = <code><a class="el" href="group__detectNet.html#ga1e79603783719e4a79f2c68f1ef47621">DETECTNET_DEFAULT_COVERAGE</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>bboxes</em> = <code><a class="el" href="group__detectNet.html#gac8c52a38ffa041865ed71fd2ea806620">DETECTNET_DEFAULT_BBOX</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>maxBatchSize</em> = <code><a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td>
          <td class="paramname"><em>precision</em> = <code><a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a>&#160;</td>
          <td class="paramname"><em>device</em> = <code><a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>allowGPUFallback</em> = <code>true</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
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<p>Load a custom network instance. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">prototxt_path</td><td>File path to the deployable network prototxt </td></tr>
    <tr><td class="paramname">model_path</td><td>File path to the caffemodel </td></tr>
    <tr><td class="paramname">mean_pixel</td><td>Input transform subtraction value (use 0.0 if the network already does this) </td></tr>
    <tr><td class="paramname">class_labels</td><td>File path to list of class name labels </td></tr>
    <tr><td class="paramname">threshold</td><td>default minimum threshold for detection </td></tr>
    <tr><td class="paramname">input</td><td>Name of the input layer blob. </td></tr>
    <tr><td class="paramname">coverage</td><td>Name of the output coverage classifier layer blob, which contains the confidence values for each bbox. </td></tr>
    <tr><td class="paramname">bboxes</td><td>Name of the output bounding box layer blob, which contains a grid of rectangles in the image. </td></tr>
    <tr><td class="paramname">maxBatchSize</td><td>The maximum batch size that the network will support and be optimized for. </td></tr>
  </table>
  </dd>
</dl>

</div>
</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a0543ed1697d294654ebfea175f1431e3">&#9670;&nbsp;</a></span>Create() <span class="overload">[5/6]</span></h2>

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          <td class="memname">static <a class="el" href="group__detectNet.html#classdetectNet">detectNet</a>* detectNet::Create </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="group__commandLine.html#classcommandLine">commandLine</a> &amp;&#160;</td>
          <td class="paramname"><em>cmdLine</em></td><td>)</td>
          <td></td>
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<p>Load a new network instance by parsing the command line. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#aeca3a465ca3fa41d8e184025c0cbbd8c">&#9670;&nbsp;</a></span>Create() <span class="overload">[6/6]</span></h2>

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          <td class="memname">static <a class="el" href="group__detectNet.html#classdetectNet">detectNet</a>* detectNet::Create </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>argc</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">char **&#160;</td>
          <td class="paramname"><em>argv</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
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<p>Load a new network instance by parsing the command line. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#ae29e82df844140f88d4d369f56886142">&#9670;&nbsp;</a></span>Detect() <span class="overload">[1/6]</span></h2>

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          <td class="memname">int detectNet::Detect </td>
          <td>(</td>
          <td class="paramtype">float *&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>width</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="structdetectNet_1_1Detection.html">Detection</a> **&#160;</td>
          <td class="paramname"><em>detections</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>overlay</em> = <code><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a></code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Detect object locations from an RGBA image, returning an array containing the detection results. </p>
<dl class="deprecated"><dt><b><a class="el" href="deprecated.html#_deprecated000003">Deprecated:</a></b></dt><dd>this overload of <a class="el" href="group__detectNet.html#a158f5cf9d366668b8229375c1a342a04" title="Detect object locations from an image, returning an array containing the detection results.">Detect()</a> provides legacy compatibility with <code>float*</code> type (RGBA32F). </dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>float4 RGBA input image in CUDA device memory. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">width</td><td>width of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">height</td><td>height of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">detections</td><td>pointer that will be set to array of detection results (residing in shared CPU/GPU memory) </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">overlay</td><td>bitwise OR combination of overlay flags (</td></tr>
  </table>
  </dd>
</dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6" title="Overlay flags (can be OR&#39;d together).">OverlayFlags</a> and </dd>
<dd>
<a class="el" href="group__detectNet.html#a94ff26d023e725a5c2da5be4f8d237d5" title="Draw the detected bounding boxes overlayed on an RGBA image.">Overlay()</a>), or <a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6a966eaaaa69ed08d36dcb862e3fc644bf" title="No overlay.">OVERLAY_NONE</a>. </dd></dl>
<dl class="section return"><dt>Returns</dt><dd>The number of detected objects, 0 if there were no detected objects, and -1 if an error was encountered. </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#a20ceb077cd42eaa2ebc0bdb31e68e10c">&#9670;&nbsp;</a></span>Detect() <span class="overload">[2/6]</span></h2>

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          <td class="memname">int detectNet::Detect </td>
          <td>(</td>
          <td class="paramtype">float *&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>width</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *&#160;</td>
          <td class="paramname"><em>detections</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>overlay</em> = <code><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a></code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Detect object locations in an RGBA image, into an array of the results allocated by the user. </p>
<dl class="deprecated"><dt><b><a class="el" href="deprecated.html#_deprecated000004">Deprecated:</a></b></dt><dd>this overload of <a class="el" href="group__detectNet.html#a158f5cf9d366668b8229375c1a342a04" title="Detect object locations from an image, returning an array containing the detection results.">Detect()</a> provides legacy compatibility with <code>float*</code> type (RGBA32F). </dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>float4 RGBA input image in CUDA device memory. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">width</td><td>width of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">height</td><td>height of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">detections</td><td>pointer to user-allocated array that will be filled with the detection results. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="group__detectNet.html#a38689b7d4b83723038677d66c1961974" title="Retrieve the maximum number of simultaneous detections the network supports.">GetMaxDetections()</a> for the number of detection results that should be allocated in this buffer. </dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">overlay</td><td>bitwise OR combination of overlay flags (</td></tr>
  </table>
  </dd>
</dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6" title="Overlay flags (can be OR&#39;d together).">OverlayFlags</a> and </dd>
<dd>
<a class="el" href="group__detectNet.html#a94ff26d023e725a5c2da5be4f8d237d5" title="Draw the detected bounding boxes overlayed on an RGBA image.">Overlay()</a>), or <a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6a966eaaaa69ed08d36dcb862e3fc644bf" title="No overlay.">OVERLAY_NONE</a>. </dd></dl>
<dl class="section return"><dt>Returns</dt><dd>The number of detected objects, 0 if there were no detected objects, and -1 if an error was encountered. </dd></dl>

</div>
</div>
<a id="a158f5cf9d366668b8229375c1a342a04"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a158f5cf9d366668b8229375c1a342a04">&#9670;&nbsp;</a></span>Detect() <span class="overload">[3/6]</span></h2>

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template&lt;typename T &gt; </div>
<table class="mlabels">
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  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">int detectNet::Detect </td>
          <td>(</td>
          <td class="paramtype">T *&#160;</td>
          <td class="paramname"><em>image</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>width</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="structdetectNet_1_1Detection.html">Detection</a> **&#160;</td>
          <td class="paramname"><em>detections</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>overlay</em> = <code><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a></code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Detect object locations from an image, returning an array containing the detection results. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>input image in CUDA device memory (uchar3/uchar4/float3/float4) </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">width</td><td>width of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">height</td><td>height of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">detections</td><td>pointer that will be set to array of detection results (residing in shared CPU/GPU memory) </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">overlay</td><td>bitwise OR combination of overlay flags (</td></tr>
  </table>
  </dd>
</dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6" title="Overlay flags (can be OR&#39;d together).">OverlayFlags</a> and </dd>
<dd>
<a class="el" href="group__detectNet.html#a94ff26d023e725a5c2da5be4f8d237d5" title="Draw the detected bounding boxes overlayed on an RGBA image.">Overlay()</a>), or <a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6a966eaaaa69ed08d36dcb862e3fc644bf" title="No overlay.">OVERLAY_NONE</a>. </dd></dl>
<dl class="section return"><dt>Returns</dt><dd>The number of detected objects, 0 if there were no detected objects, and -1 if an error was encountered. </dd></dl>

</div>
</div>
<a id="a3cd0725bfab4ec8a54192eae4c7bac45"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a3cd0725bfab4ec8a54192eae4c7bac45">&#9670;&nbsp;</a></span>Detect() <span class="overload">[4/6]</span></h2>

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        <tr>
          <td class="memname">int detectNet::Detect </td>
          <td>(</td>
          <td class="paramtype">T *&#160;</td>
          <td class="paramname"><em>image</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>width</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *&#160;</td>
          <td class="paramname"><em>detections</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>overlay</em> = <code><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a></code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Detect object locations in an image, into an array of the results allocated by the user. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>input image in CUDA device memory (uchar3/uchar4/float3/float4) </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">width</td><td>width of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">height</td><td>height of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">detections</td><td>pointer to user-allocated array that will be filled with the detection results. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="group__detectNet.html#a38689b7d4b83723038677d66c1961974" title="Retrieve the maximum number of simultaneous detections the network supports.">GetMaxDetections()</a> for the number of detection results that should be allocated in this buffer. </dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">overlay</td><td>bitwise OR combination of overlay flags (</td></tr>
  </table>
  </dd>
</dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6" title="Overlay flags (can be OR&#39;d together).">OverlayFlags</a> and </dd>
<dd>
<a class="el" href="group__detectNet.html#a94ff26d023e725a5c2da5be4f8d237d5" title="Draw the detected bounding boxes overlayed on an RGBA image.">Overlay()</a>), or <a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6a966eaaaa69ed08d36dcb862e3fc644bf" title="No overlay.">OVERLAY_NONE</a>. </dd></dl>
<dl class="section return"><dt>Returns</dt><dd>The number of detected objects, 0 if there were no detected objects, and -1 if an error was encountered. </dd></dl>

</div>
</div>
<a id="a3974d2ac560544539ba98b48fa736f4e"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a3974d2ac560544539ba98b48fa736f4e">&#9670;&nbsp;</a></span>Detect() <span class="overload">[5/6]</span></h2>

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      <table class="memname">
        <tr>
          <td class="memname">int detectNet::Detect </td>
          <td>(</td>
          <td class="paramtype">void *&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>width</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a>&#160;</td>
          <td class="paramname"><em>format</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="structdetectNet_1_1Detection.html">Detection</a> **&#160;</td>
          <td class="paramname"><em>detections</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>overlay</em> = <code><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a></code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Detect object locations from an image, returning an array containing the detection results. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>input image in CUDA device memory (uchar3/uchar4/float3/float4) </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">width</td><td>width of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">height</td><td>height of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">detections</td><td>pointer that will be set to array of detection results (residing in shared CPU/GPU memory) </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">overlay</td><td>bitwise OR combination of overlay flags (</td></tr>
  </table>
  </dd>
</dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6" title="Overlay flags (can be OR&#39;d together).">OverlayFlags</a> and </dd>
<dd>
<a class="el" href="group__detectNet.html#a94ff26d023e725a5c2da5be4f8d237d5" title="Draw the detected bounding boxes overlayed on an RGBA image.">Overlay()</a>), or <a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6a966eaaaa69ed08d36dcb862e3fc644bf" title="No overlay.">OVERLAY_NONE</a>. </dd></dl>
<dl class="section return"><dt>Returns</dt><dd>The number of detected objects, 0 if there were no detected objects, and -1 if an error was encountered. </dd></dl>

</div>
</div>
<a id="a49c6fcf052819b9980fd9d72b09d26c3"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a49c6fcf052819b9980fd9d72b09d26c3">&#9670;&nbsp;</a></span>Detect() <span class="overload">[6/6]</span></h2>

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        <tr>
          <td class="memname">int detectNet::Detect </td>
          <td>(</td>
          <td class="paramtype">void *&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>width</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a>&#160;</td>
          <td class="paramname"><em>format</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *&#160;</td>
          <td class="paramname"><em>detections</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>overlay</em> = <code><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a></code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Detect object locations from an image, into an array of the results allocated by the user. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>input image in CUDA device memory (uchar3/uchar4/float3/float4) </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">width</td><td>width of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">height</td><td>height of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">detections</td><td>pointer to user-allocated array that will be filled with the detection results. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="group__detectNet.html#a38689b7d4b83723038677d66c1961974" title="Retrieve the maximum number of simultaneous detections the network supports.">GetMaxDetections()</a> for the number of detection results that should be allocated in this buffer. </dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">overlay</td><td>bitwise OR combination of overlay flags (</td></tr>
  </table>
  </dd>
</dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6" title="Overlay flags (can be OR&#39;d together).">OverlayFlags</a> and </dd>
<dd>
<a class="el" href="group__detectNet.html#a94ff26d023e725a5c2da5be4f8d237d5" title="Draw the detected bounding boxes overlayed on an RGBA image.">Overlay()</a>), or <a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6a966eaaaa69ed08d36dcb862e3fc644bf" title="No overlay.">OVERLAY_NONE</a>. </dd></dl>
<dl class="section return"><dt>Returns</dt><dd>The number of detected objects, 0 if there were no detected objects, and -1 if an error was encountered. </dd></dl>

</div>
</div>
<a id="ad099b492b82a1d7a55955ecf990fbea8"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ad099b492b82a1d7a55955ecf990fbea8">&#9670;&nbsp;</a></span>GetClassColor()</h2>

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          <td class="memname">float4 detectNet::GetClassColor </td>
          <td>(</td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>classIndex</em></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
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<p>Retrieve the RGBA visualization color a particular class. </p>

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<a id="a6aeee309257b1a42df275fdfe948a585"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a6aeee309257b1a42df275fdfe948a585">&#9670;&nbsp;</a></span>GetClassDesc()</h2>

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          <td class="memname">const char* detectNet::GetClassDesc </td>
          <td>(</td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>index</em></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Retrieve the description of a particular class. </p>

</div>
</div>
<a id="a80524a777ce4fc368922ac3cd8f97973"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a80524a777ce4fc368922ac3cd8f97973">&#9670;&nbsp;</a></span>GetClassLabel()</h2>

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          <td class="memname">const char* detectNet::GetClassLabel </td>
          <td>(</td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>index</em></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Retrieve the description of a particular class. </p>

</div>
</div>
<a id="a8085235ab275b1bad3dcde14ec1223ed"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a8085235ab275b1bad3dcde14ec1223ed">&#9670;&nbsp;</a></span>GetClassPath()</h2>

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          <td class="memname">const char* detectNet::GetClassPath </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
        </tr>
      </table>
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<p>Retrieve the path to the file containing the class descriptions. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a68322545826d7b866a70d5f9758f4d48">&#9670;&nbsp;</a></span>GetClassSynset()</h2>

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<p>Retrieve the class synset category of a particular class. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#ae63aba1ee4e12f8afab35f0c928f4ef6">&#9670;&nbsp;</a></span>GetClusteringThreshold()</h2>

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<p>Retrieve the overlapping area % threshold for clustering. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a9bb59636a3a0ba168752bb5e9f76421c">&#9670;&nbsp;</a></span>GetConfidenceThreshold()</h2>

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<p>Retrieve the minimum threshold for detection. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a6259a0c37ec3a3246bf23aad152c69e4">&#9670;&nbsp;</a></span>GetLineWidth()</h2>

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<p>Retrieve the line width used during overlay when OVERLAY_LINES is used. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a38689b7d4b83723038677d66c1961974">&#9670;&nbsp;</a></span>GetMaxDetections()</h2>

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<p>Retrieve the maximum number of simultaneous detections the network supports. </p>
<p>Knowing this is useful for allocating the buffers to store the output detection results. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#acec72d01e020cf5bc50747abb0245f06">&#9670;&nbsp;</a></span>GetNumClasses()</h2>

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<p>Retrieve the number of object classes supported in the detector. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#aefb9214feb29121368dfaa5cf4082565">&#9670;&nbsp;</a></span>GetOverlayAlpha()</h2>

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<p>Retrieve the overlay alpha blending value for classes that don't have it explicitly set (between 0-255). </p>

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<h2 class="memtitle"><span class="permalink"><a href="#ad50b3774ee7350d0c256d94a785ba9df">&#9670;&nbsp;</a></span>GetThreshold()</h2>

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<p>Retrieve the minimum threshold for detection. </p>
<dl class="deprecated"><dt><b><a class="el" href="deprecated.html#_deprecated000005">Deprecated:</a></b></dt><dd>please use <a class="el" href="group__detectNet.html#a9bb59636a3a0ba168752bb5e9f76421c" title="Retrieve the minimum threshold for detection.">GetConfidenceThreshold()</a> instead </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#a130307a2bac02b13834116eb72728530">&#9670;&nbsp;</a></span>GetTracker()</h2>

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<p>Get the object tracker being used. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a77af699123da152d3e83ca355ed4de11">&#9670;&nbsp;</a></span>init()</h2>

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          <td class="paramname"><em>prototxt_path</em>, </td>
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          <td class="paramname"><em>model_path</em>, </td>
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          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>class_labels</em>, </td>
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          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>class_colors</em>, </td>
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          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>threshold</em>, </td>
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          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>input</em>, </td>
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          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>coverage</em>, </td>
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          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>bboxes</em>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>maxBatchSize</em>, </td>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td>
          <td class="paramname"><em>precision</em>, </td>
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          <td></td>
          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a>&#160;</td>
          <td class="paramname"><em>device</em>, </td>
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          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>allowGPUFallback</em>&#160;</td>
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          <td>)</td>
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<h2 class="memtitle"><span class="permalink"><a href="#a2c649836482b3bfe12f4ab671387aa77">&#9670;&nbsp;</a></span>loadClassColors()</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#a5ab26d1cfb1710a826cae4473c7173f5">&#9670;&nbsp;</a></span>loadClassInfo()</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#a94ff26d023e725a5c2da5be4f8d237d5">&#9670;&nbsp;</a></span>Overlay() <span class="overload">[1/2]</span></h2>

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          <td class="paramname"><em>output</em>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>width</em>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>, </td>
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          <td class="paramtype"><a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *&#160;</td>
          <td class="paramname"><em>detections</em>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>numDetections</em>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>flags</em> = <code><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a></code>&#160;</td>
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<p>Draw the detected bounding boxes overlayed on an RGBA image. </p>
<dl class="section note"><dt>Note</dt><dd><a class="el" href="group__detectNet.html#a94ff26d023e725a5c2da5be4f8d237d5" title="Draw the detected bounding boxes overlayed on an RGBA image.">Overlay()</a> will automatically be called by default by <a class="el" href="group__detectNet.html#a158f5cf9d366668b8229375c1a342a04" title="Detect object locations from an image, returning an array containing the detection results.">Detect()</a>, if the overlay parameter is true </dd></dl>
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    <tr><td class="paramname">input</td><td>input image in CUDA device memory. </td></tr>
    <tr><td class="paramname">output</td><td>output image in CUDA device memory. </td></tr>
    <tr><td class="paramname">detections</td><td>Array of detections allocated in CUDA device memory. </td></tr>
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<h2 class="memtitle"><span class="permalink"><a href="#af6bfc51166410901174b48f99f59f85b">&#9670;&nbsp;</a></span>Overlay() <span class="overload">[2/2]</span></h2>

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          <td class="paramname"><em>height</em>, </td>
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          <td></td>
          <td class="paramtype"><a class="el" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a>&#160;</td>
          <td class="paramname"><em>format</em>, </td>
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          <td class="paramtype"><a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *&#160;</td>
          <td class="paramname"><em>detections</em>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>numDetections</em>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>flags</em> = <code><a class="el" href="group__detectNet.html#a29e74cde23a8dd541dbd848e457663d6ae65b9f9d5e1d39f02183f5f0f9816dfd">OVERLAY_DEFAULT</a></code>&#160;</td>
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<p>Draw the detected bounding boxes overlayed on an RGBA image. </p>
<dl class="section note"><dt>Note</dt><dd><a class="el" href="group__detectNet.html#a94ff26d023e725a5c2da5be4f8d237d5" title="Draw the detected bounding boxes overlayed on an RGBA image.">Overlay()</a> will automatically be called by default by <a class="el" href="group__detectNet.html#a158f5cf9d366668b8229375c1a342a04" title="Detect object locations from an image, returning an array containing the detection results.">Detect()</a>, if the overlay parameter is true </dd></dl>
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    <tr><td class="paramname">input</td><td>input image in CUDA device memory. </td></tr>
    <tr><td class="paramname">output</td><td>output image in CUDA device memory. </td></tr>
    <tr><td class="paramname">detections</td><td>Array of detections allocated in CUDA device memory. </td></tr>
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<h2 class="memtitle"><span class="permalink"><a href="#a2bb2b446e26a466c9e355f1472f68e4c">&#9670;&nbsp;</a></span>OverlayFlagsFromStr()</h2>

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<p>Parse a string sequence into OverlayFlags enum. </p>
<p>Valid flags are "none", "box", "label", and "conf" and it is possible to combine flags (bitwise OR) together with commas or pipe (|) symbol. For example, the string sequence "box,label,conf" would return the flags <code>OVERLAY_BOX|OVERLAY_LABEL|OVERLAY_CONFIDENCE</code>. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a283969d1290eb6675356b7a96ecb89e6">&#9670;&nbsp;</a></span>postProcess()</h2>

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          <td class="paramname"><em>height</em>, </td>
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<h2 class="memtitle"><span class="permalink"><a href="#ad3f419e16f510e8c3a21084b0f99719d">&#9670;&nbsp;</a></span>postProcessDetectNet()</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#a7eacf562b466e13af2303f0360543a77">&#9670;&nbsp;</a></span>postProcessDetectNet_v2()</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#a41a6499dc49c6598b2ed4bbfb7053898">&#9670;&nbsp;</a></span>postProcessSSD_ONNX()</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#a3692a43f224a5b9178203a3e6c6d7b51">&#9670;&nbsp;</a></span>postProcessSSD_UFF()</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#a54aa7f77769d35fbd92d205904d364a2">&#9670;&nbsp;</a></span>preProcess()</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#afac4f7b876a6ee391599bbce3f2982b1">&#9670;&nbsp;</a></span>SetClassColor() <span class="overload">[1/2]</span></h2>

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<p>Set the visualization color of a particular class of object. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#ab9ba7c63d2fc417d0908324fd0dc2223">&#9670;&nbsp;</a></span>SetClassColor() <span class="overload">[2/2]</span></h2>

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<p>Set the visualization color of a particular class of object. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a43266dab4ba9e3115c44df93ee2f34d8">&#9670;&nbsp;</a></span>SetClusteringThreshold()</h2>

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<p>Set the overlapping area % threshold for clustering. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a7d9740240d5def31cdbb92c90fb3f26f">&#9670;&nbsp;</a></span>SetConfidenceThreshold()</h2>

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<p>Set the minimum threshold for detection. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a0e8b7f336a5531ce9446b4c63e7143e4">&#9670;&nbsp;</a></span>SetLineWidth()</h2>

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<p>Set the line width used during overlay when OVERLAY_LINES is used. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#ac2ffd50b5fb52d6ceea09af29b144afc">&#9670;&nbsp;</a></span>SetOverlayAlpha()</h2>

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          <td class="memname">void detectNet::SetOverlayAlpha </td>
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<p>Set overlay alpha blending value for all classes (between 0-255). </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a1cb45695bf95d5fb5db48976d1de8ae2">&#9670;&nbsp;</a></span>SetThreshold()</h2>

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<p>Set the minimum threshold for detection. </p>
<dl class="deprecated"><dt><b><a class="el" href="deprecated.html#_deprecated000006">Deprecated:</a></b></dt><dd>please use <a class="el" href="group__detectNet.html#a7d9740240d5def31cdbb92c90fb3f26f" title="Set the minimum threshold for detection.">SetConfidenceThreshold()</a> instead </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#a83cf2684627e8b6f3d14eee8f02def61">&#9670;&nbsp;</a></span>SetTracker()</h2>

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          <td class="paramtype"><a class="el" href="group__objectTracker.html#classobjectTracker">objectTracker</a> *&#160;</td>
          <td class="paramname"><em>tracker</em></td><td>)</td>
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<p>Set the object tracker to be used. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#ae25ca7f51a3235011442ac1edeafe1d8">&#9670;&nbsp;</a></span>sortDetections()</h2>

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          <td class="paramname"><em>detections</em>, </td>
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          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>numDetections</em>&#160;</td>
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<h2 class="memtitle"><span class="permalink"><a href="#a8c336e6b018a780e7ca74897d1bce628">&#9670;&nbsp;</a></span>Usage()</h2>

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<p>Usage string for command line arguments to <a class="el" href="group__detectNet.html#a468b5e636096159500f3db327c31e8dd" title="Load a pre-trained model.">Create()</a> </p>

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<h4 class="groupheader">Member Data Documentation</h4>
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<h2 class="memtitle"><span class="permalink"><a href="#aa29864c6477dd396d5054a67d7406b9f">&#9670;&nbsp;</a></span>mClassColors</h2>

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<h2 class="groupheader">Macro Definition Documentation</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#ga98aed3513105a81bd86361eef5423383">&#9670;&nbsp;</a></span>DETECTNET_DEFAULT_ALPHA</h2>

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<p>Default alpha blending value used during overlay. </p>

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<p>Name of default output blob of the grid of bounding boxes for DetectNet caffe model. </p>

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<p>Default value of the clustering area-of-overlap threshold. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#ga56785ae0440a9e281a6dc44dc01039a5">&#9670;&nbsp;</a></span>DETECTNET_DEFAULT_CONFIDENCE_THRESHOLD</h2>

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<p>Default value of the minimum detection threshold. </p>

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<p>Name of default output blob of the coverage map for DetectNet caffe model. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#gac824e329015dc8aed6e1112bfe21cb97">&#9670;&nbsp;</a></span>DETECTNET_DEFAULT_INPUT</h2>

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<p>Name of default input blob for DetectNet caffe model. </p>

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<p>Default value of the minimum detection threshold. </p>
<dl class="deprecated"><dt><b><a class="el" href="deprecated.html#_deprecated000002">Deprecated:</a></b></dt><dd>please use DETECTNET_DEFAULT_CONFIDENCE_THRESHOLD instead </dd></dl>

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<p>The model type for <a class="el" href="group__detectNet.html#classdetectNet" title="Object recognition and localization networks with TensorRT support.">detectNet</a> in data/networks/models.json. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#gad5f43885a04689f10c6f9d297ab88a8d">&#9670;&nbsp;</a></span>DETECTNET_USAGE_STRING</h2>

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<b>Value:</b><div class="fragment"><div class="line">                  <span class="stringliteral">&quot;detectNet arguments: \n&quot;</span>                                     \</div>
<div class="line">                  <span class="stringliteral">&quot;  --network=NETWORK     pre-trained model to load, one of the following:\n&quot;</span>          \</div>
<div class="line">                  <span class="stringliteral">&quot;                            * ssd-mobilenet-v1\n&quot;</span>                                                    \</div>
<div class="line">                  <span class="stringliteral">&quot;                            * ssd-mobilenet-v2 (default)\n&quot;</span>                                  \</div>
<div class="line">                  <span class="stringliteral">&quot;                            * ssd-inception-v2\n&quot;</span>                                                    \</div>
<div class="line">                  <span class="stringliteral">&quot;                            * peoplenet\n&quot;</span>                                        \</div>
<div class="line">                  <span class="stringliteral">&quot;                            * peoplenet-pruned\n&quot;</span>                                 \</div>
<div class="line">                  <span class="stringliteral">&quot;                            * dashcamnet\n&quot;</span>                                       \</div>
<div class="line">                  <span class="stringliteral">&quot;                            * trafficcamnet\n&quot;</span>                                    \</div>
<div class="line">                  <span class="stringliteral">&quot;                            * facedetect\n&quot;</span>                                       \</div>
<div class="line">                  <span class="stringliteral">&quot;  --model=MODEL         path to custom model to load (caffemodel, uff, or onnx)\n&quot;</span>                                   \</div>
<div class="line">                  <span class="stringliteral">&quot;  --prototxt=PROTOTXT   path to custom prototxt to load (for .caffemodel only)\n&quot;</span>                                    \</div>
<div class="line">                  <span class="stringliteral">&quot;  --labels=LABELS       path to text file containing the labels for each class\n&quot;</span>                                    \</div>
<div class="line">                  <span class="stringliteral">&quot;  --input-blob=INPUT    name of the input layer (default is &#39;&quot;</span> <a class="code" href="group__detectNet.html#gac824e329015dc8aed6e1112bfe21cb97">DETECTNET_DEFAULT_INPUT</a> <span class="stringliteral">&quot;&#39;)\n&quot;</span>                        \</div>
<div class="line">                  <span class="stringliteral">&quot;  --output-cvg=COVERAGE name of the coverage/confidence output layer (default is &#39;&quot;</span> <a class="code" href="group__detectNet.html#ga1e79603783719e4a79f2c68f1ef47621">DETECTNET_DEFAULT_COVERAGE</a> <span class="stringliteral">&quot;&#39;)\n&quot;</span>        \</div>
<div class="line">                  <span class="stringliteral">&quot;  --output-bbox=BOXES   name of the bounding output layer (default is &#39;&quot;</span> <a class="code" href="group__detectNet.html#gac8c52a38ffa041865ed71fd2ea806620">DETECTNET_DEFAULT_BBOX</a> <span class="stringliteral">&quot;&#39;)\n&quot;</span>       \</div>
<div class="line">                  <span class="stringliteral">&quot;  --mean-pixel=PIXEL    mean pixel value to subtract from input (default is 0.0)\n&quot;</span>                                  \</div>
<div class="line">                  <span class="stringliteral">&quot;  --confidence=CONF     minimum confidence threshold for detection (default is 0.5)\n&quot;</span>                               \</div>
<div class="line">                  <span class="stringliteral">&quot;  --clustering=CLUSTER  minimum overlapping area threshold for clustering (default is 0.75)\n&quot;</span>             \</div>
<div class="line">            <span class="stringliteral">&quot;  --alpha=ALPHA         overlay alpha blending value, range 0-255 (default: 120)\n&quot;</span>                                        \</div>
<div class="line">                  <span class="stringliteral">&quot;  --overlay=OVERLAY     detection overlay flags (e.g. --overlay=box,labels,conf)\n&quot;</span>                                  \</div>
<div class="line">                  <span class="stringliteral">&quot;                        valid combinations are:  &#39;box&#39;, &#39;lines&#39;, &#39;labels&#39;, &#39;conf&#39;, &#39;none&#39;\n&quot;</span>                 \</div>
<div class="line">                  <span class="stringliteral">&quot;  --profile             enable layer profiling in TensorRT\n\n&quot;</span>                              \</div>
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<p>Standard command-line options able to be passed to <a class="el" href="group__detectNet.html#a468b5e636096159500f3db327c31e8dd" title="Load a pre-trained model.">detectNet::Create()</a> </p>

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<div class="ttc" id="agroup__detectNet_html_ga1e79603783719e4a79f2c68f1ef47621"><div class="ttname"><a href="group__detectNet.html#ga1e79603783719e4a79f2c68f1ef47621">DETECTNET_DEFAULT_COVERAGE</a></div><div class="ttdeci">#define DETECTNET_DEFAULT_COVERAGE</div><div class="ttdoc">Name of default output blob of the coverage map for DetectNet caffe model.</div><div class="ttdef"><b>Definition:</b> detectNet.h:40</div></div>
<div class="ttc" id="agroup__detectNet_html_gac824e329015dc8aed6e1112bfe21cb97"><div class="ttname"><a href="group__detectNet.html#gac824e329015dc8aed6e1112bfe21cb97">DETECTNET_DEFAULT_INPUT</a></div><div class="ttdeci">#define DETECTNET_DEFAULT_INPUT</div><div class="ttdoc">Name of default input blob for DetectNet caffe model.</div><div class="ttdef"><b>Definition:</b> detectNet.h:34</div></div>
<div class="ttc" id="agroup__detectNet_html_gac8c52a38ffa041865ed71fd2ea806620"><div class="ttname"><a href="group__detectNet.html#gac8c52a38ffa041865ed71fd2ea806620">DETECTNET_DEFAULT_BBOX</a></div><div class="ttdeci">#define DETECTNET_DEFAULT_BBOX</div><div class="ttdoc">Name of default output blob of the grid of bounding boxes for DetectNet caffe model.</div><div class="ttdef"><b>Definition:</b> detectNet.h:46</div></div>
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